Assessment of ambient air quality in heavy industrial localities: a case study of Yanbu industrial city

Abstract

The objective of this study was to provide an appropriate evaluation of ambient air quality in industrial localities and the surrounding residential areas in its vicinity. Therefore, an assessment of gaseous emissions from industrial sectors was performed. For this purpose, concentrations were measured for SO₂, H₂S, NO₂, O₃, CO, PM_2.5, and PM₁₀ in five spatially diverse monitoring stations (AQMS) over different temporal intervals (daily, monthly, and annual) for the years 2015–2020. The impact on the environment and public health was evaluated through comparison with the corresponding regional and international standards. In the case study region, a substantial spatiotemporal variation was observed in the gaseous contaminants, due to the predominance of characterized meteorological parameters interfering with contributions from existing chemical facilities and anthropogenic activities. The exceedances for the investigated emissions were routinely violated the standard concentrations. According to AQI classifications, these violations were assigned to be within the acceptable limits for the gaseous emissions, moderately polluted for PM_2.5, and unhealthy for sensitive groups for PM10. The proper distribution of the AQMSs within the industrial locality provides enough spatial and temporal observatory data, such that the exceedances were reduced with the subsequent years, hence appropriate evaluation of the relevant measurements revealed effective qualitative policies taken into action by authorities to maintain less accumulation of the gaseous emissions into ambient air beyond the harmful limits for public health and environment.

Introduction

Nowadays, ambient air quality suffers a great challenge, that is, remediation of contaminated effluents produced from domestic, agricultural, and industrial activities due to gaseous emissions. To insure a more suitable outdoor air quality, specifically for industrial landscapes associated with vicinity residential areas, air pollution regulatory thresholds were correspondingly published to be regionally as well as internationally implemented to provide an assessment of health effects for serious health–harmful pollution levels in the industrial sectors. Parametric investigations were appealed by Aina et al. (2014) to extensively highlight findings on PM_2.5 in several KSA cities within the years 2001–2010 (urbanized and industrialized) by means of mobile observatory stations, to highlight the trend of PM_2.5 ground observations with which visualizations were combined for the purpose of data validation. This approach suffers from some obstacles especially on cloudy and dusty days. Meanwhile, Al-Azmi et al. (2009) compared measured NO_x and SO₂ inventories by static methods with short-term model predictions for 2 years in Kuwait power plants under current meteorological conditions, and promising results were only obtained with hourly measurements, leading to misleading long-term observations. In a relevant study, Al-Zboon et al. (2022) studied the prediction of ground levels of SO₂, NO₂, CO, and PM₁₀ emissions from a thermal desalination plant in the western region of KSA with respect to reference observatory data, where the AERMOD Dispersion Model was applied to investigate the short-term health impact on an inhibited area restricted of 50 km by 50 km from the plant. Meanwhile, Alharbi et al. (2014) conducted their investigations on O₃, CO, NO₂, SO₂, H₂S, and PM₁₀ within the period 1999–2004, obtained from 5 static AQMSs in King Abdulaziz City for Science and Technology (KACST) in Riyadh; subsequently, latterly PM₁₀ and smog (metals and ions) were studied by Alharbi et al. (2015). Their investigations revealed that the concentration ratios of certain contaminants such as metalloids could be influential on the transportation scenarios for outdoor pollutants in residential areas. Al-Harbi et al. (2020) investigated the temporal variations for NO, NO₂, SO₂, and O₃ by mobile measuring stations located in Ras Al-Khafji during the year 2013 for three monitoring areas (residential, industrial, and terminal). Also, a correlation study revealed that the concentrations of these contaminants clearly vary during the day and that the pattern and movement of air pollutants were significantly influenced by meteorological conditions. At observatory sites, non-buoyant ground-level sources, including home heating and traffic pollution, were regarded as the main sources of emissions. It should be focused on combination of different meteorological circumstances, within a massive industrial locality and intensive anthropogenic patterns on outdoor observations. Another factor to consider is the presence of a combination of different meteorological circumstances. A related study by Al-Jeelani (2014) was performed to examine spatiotemporal outdoor gaseous NO, NO₂, NO_x, O₃, CO, SO₂, CH₄, TNMHCs (non-methane hydrocarbons), and other volatile organic compounds (VOCs) obtained from static observatory sites. However, within the Yanbu Industrial Complex, no significant variations were obtained for TNMHCs and CH₄ as were obtained for the other contaminants. Therefore, long-term management programs were recommended for maintaining outdoor air quality in the proper condition for life and work.

For the special annual occasions of al-Fitr and al-Adha festivals, Ramadan, and the Hajj periods in KSA, when many cities in KSA are usually crowded with residents, visitors, and pilgrims for relatively short term durations of some days to few months, researchers extensively focused on the occurrence of outdoor emissions (Butenhoff et al., 2015; Dasari et al., 2020; Habeebullah, 2013; Hassan et al., 2013; Pasha & Alharbi, 2015; Shareef et al., 2018; Shareef et al., 2018). Anthropogenic activities have a particularly significant impact that coincides with seasonal phenomena like thermal boundary layer mixing on the widespread gaseous emissions with harmful limits in different KSA communities at a particular time. For long-term durations such as investigations by Hassan et al. (2019) and Khalil et al. (2016), it was revealed that the surface temperature expedited the photochemical process of O₃ precursors to produce secondary emissions. Along with investigations on suspended particulate matters (Alharbi et al., 2015; Modaihsh et al., 2015; Pasha & Alharbi, 2015) when seasonal dusty winds have an influence on the buoyancy of contaminants to upper atmospheric layers as well as transportation to neighboring regions apart from emission sources, even more promising findings were highlighted in this domain for Egypt (Hindy & Abdelmaksoud, 2016), other sub-regions in the Middle East (Hariri et al., 2013; Uhkov et al., 2020), and Spain (Rovira et al., 2021).

In the literature, model data were used to predict the spatial and temporal variation in air pollutants for various air pollutants with a focus on potential risk assessment (Aina et al., 2014; Dasari et al., 2020; Munir et al., 2013; Munir et al., 2016; Uhkov et al., 2020). As such, studies have implied model predictions for outdoor contaminations, although they have shown apparent discrepancies between satellite-derived data and the in situ measurements. The reasons behind these discrepancies were attributed, from one side, to the exclusion of metrological factors like ground temperature and topography in changing outdoor gaseous levels, and on the other side, relative concentrations of relevant O₃ precursors. In the study by Meng and Zhou (2020), reference air quality data (SO₂, soot, and other industrial gas emissions) were acquired from reports by the National Bureau of Statistics, and a STIRPAT model was used to predict the air pollution emissions in the Beijing-Tianjin-Hebei region/China. In a pertinent study by Liang and Gong (2020), air pollution was investigated to evaluate the urban landscape effects of pollutants and their associations at the county level in China over the study period (2000–2014), where the largest urban growth is anticipated to occur. The annual mean PM_2.5 concentration’s trends were determined using an empirical study and standard numerical methods, whereas Lee et al. (2014) used surface observational data of typical air pollutants (PM_2.5 and SO₂) collected from 14 static measuring sites in two mega-industrial complexes in Ulsan Province, Korea, which were simulated with the WRF-CALPUFF Model and the meteorological data were generated by CALMET software, to test the consistency of observational data with model predictions. In recent studies by Cichowicz and Dobrzański (2022a, b), the results of the OPA03 simulation were implemented to replicate actual field measurements obtained from sparsely located static and mobile observational sites near a heat and power plant and a communication route in Lodz, Poland. 3D maps were created using ArcGIS for the spatial distribution of the air contaminants (H2S, VOC, PM₁₀, and PM_2.5). These studies revealed that industrial gaseous emissions have always been critically important in determining shifts in outdoor emission levels. Generally, the economic growth in the case study area was suggested to slow down; this policy contributes to a steady boost in air quality and comparatively maintains long-term clean surrounding circumstances, conversely the influential consequences on the relevant economy. Alternately, several air dispersion models (like AERMOD, CALPUFF, ADMS, which are applying Gaussian, Lagrangian, and Eulerian approaches, respectively) are used to indirectly estimate the PM of various diameters based on the researches’ findings that there is a meaningful relation between outdoor air contaminants and PM. The meteorological, geography, effluent physical and chemical features, and other associated phenomena (co-variables) with air pollution must be considered by the air dispersion models for reliable reproductivity of observatory air quality measurements. On the other hand, these commercial air dispersion models have varying levels of complexity based on the approaches used, as well as long computation time intervals; suffer from significant deviation between their predicted results compared to observatory air quality data; and pose numerical constraints and fundamental limitation on the receptors’ number and model validity range.

To the best of the authors’ knowledge, no comprehensive study covers the impact of the Yanbu industrial city on ambient air quality; there were few contributions made for the behavioral assimilation of gaseous emissions in the Yanbu industrial complex and the surrounding area between 2015 and 2020 for the deduction of long-term management programs. The objective of the current investigation was to delineate the spatiotemporal distribution of outdoor flaring and air pollutants, namely, SO₂, H₂S, NO₂, O₃, CO, PM_2.5, and PM₁₀, within the Yanbu industrial complex and the associated residential areas. This could help to boost policies that provide an integrative assessment of relevant scientific information on gaseous emissions, collected from diverse monitoring AQMSs within the Yanbu industrial complex. Focusing on the basic criteria for air quality standards (the in situ concentration indicators, periodic intervals, and spatial distribution from the emissions source along with the meteorological circumstances), such criteria would evaluate the health protection afforded by the relevant outdoor air standards.

Methodology and materials

Case study area

Yanbu industrial locality, which has a current area of about 185 km², is located about 350 km northwest of Jeddah on the Red Sea coast of Saudi Arabia. The remaining land consists of urban and desert regions, with industrial sectors making up about 18% of its total area. It contains many heavy industries and includes the footprints of huge manufacturers like Saudi Aramco and SABIK. The Yanbu industrial complex is also close to the main transportation harbor and includes tremendous petrochemical projects based on crude oil processing (mainly obtained from oil reservoirs in KSA’s eastern area), besides the third largest oil refinery in the world (with a production capacity of more than 140 million tons/ annum), large technical gases facilities with associated laboratories, wide container yards, huge goods warehouses, and two oversized pipelines extending over a distance of 1200 km connecting the Arabian Gulf and the Red Sea. Additionally, Yanbu’s geography contains significant amounts of mineral resources for iron ores, phosphate, gypsum, and copper (Alharbi et al., 2015; Al-Jeelani, 2014; Khalil et al., 2016; Shareef et al., 2018), which are suitable for other light and concrete industrial campuses. The Yanbu industrial complex has become the virtue of high-speed industrialization, and the vicinity residential city is properly served with wide highways, roads, and sufficient community factors that attract many work-powers from other countries, indicating the vitality of this fast-growing city. Consequently, Yanbu is also subject to increasing obstacle regarding environment and public health, and the running industrial activities of the large refining facilities, transportation harbor, and warehouses as well as large-scale city construction require a tremendous number of heavy vehicles and machines. Conversely, residential fills and rubbles due to densely populated areas and industrial wastes are sometimes not managed early. Therefore, the underground infrastructure as well the outdoor air quality needs to be secured and periodically monitored.

Source of data

A thematic strategy for the abatement of air emissions could be comprehensively evaluated by studying the spatiotemporal variation of outdoor air emissions, and this would imply conclusive evidence to enhance the continuous monitoring of ambient air pollutants through the long-term availability of in situ measurements. Implementing this approach would assist regional authorities to abate the consequences of air pollution. Moreso, regardless of anticipated changes in outdoor emissions, implementation and evaluation of control strategies could support sustainable development by allowing for reliable assessment of outdoor air quality, proper public health concerns for nearby residential areas within industrial infrastructure, and avoiding environmental deterioration (Almetwally et al., 2020; Kuklinska et al., 2015; Tsilingiridis et al., 2002; Weng et al., 2014). The Royal Commission at Yanbu (RCY) was founded as the custodial agency to plan and boost investments, mentor and manage the energy-intensive industries through integration between customers’ needs, investors’ interests, and industry with the community’s requirements for securing public health and a safe environment. In this context, a network of various AQMSs has been installed by the RCY and distributed in spatially diverse sites within the Yanbu industrial complex to monitor air pollution emissions. These sites are illustrated in Fig. 1.

Fig. 1.

Spatial AQMS’s distribution within the infrastructure of Yanbu industrial complex and vicinity residential

As the custodial agency, the RCY creates detailed health-related assessments of several air emissions, including SO₂, H₂S, NO₂, O₃, CO, PM_2.5, and PM₁₀, and these gaseous concentrations in outdoor air were continuously measured and recorded to essentially provide evidence regarding the burden of disease estimates caused by exposures to harmful air quality, according to regional and international guidelines, through monitoring and evaluation of gaseous emissions to empower the health sector to tackle outdoor air pollution. AQMS 1 is located to the south-east and south of Yanbu vicinity residential, respectively, while AQMS 4 is located to offshore downwind of Yanbu vicinity residential. AQMSs 1 and 4 mainly monitor the gaseous emissions routing from the Light-Industry campus (containing technical gases, Gypsum facility). AQMS 3 is located to the offshore downwind of the Aramco Refinery; in the other direction, AQMS 5 is located to the west-south direction of Aramco Refinery, onshore upwind of the power station and spatially close to Aramco terminals, while AQMS 2 is located to the east of a concrete industry including handling and production construction materials and a distance about 6 km south-east from Aramco Refinery. As summarized in Table 1, it was observed that the AQMSs were spread out over vast distances ranging from few kilometers from the nearby source to few tens of kilometers apart from each individual AQMS to ensure more comprehensive evaluation of the results. Furthermore, two locations were selected near the onshore zone, while the other three were within the offshore zone. The network was designed to ensure effective coverage of air quality in both the industrial and residential landscapes of Yanbu city.

Table 1.

Distance between individual AQMSs and nearby sources

Distance (km)	AQMS1	AQMS2	AQMS3	AQMS4	AQMS5	Nearby source (distance)
AQMS1	–	15	10.7	5	7	Yanbu vicinity residential (2.6)
AQMS2	15	–	5	16.8	9.5	Coment industry (1.3)
AQMS3	10.7	5	–	11.8	7.2	Aramco Refinery (2.4)
AQMS4	5	16.8	11.8	–	12.3	Light industry (3.8)
AQMS5	7	9.5	7.2	12.3	–	Power station and Aramco Terminals (2.0)

Surface measurements for outdoor emissions including SO₂, H₂S, NO₂, O₃, CO, PM_2.5, and PM₁₀ were obtained, and the gaseous concentrations in five outdoor air stations were collected from RCY reports over the years 2015–2020. In specific, AQMS 1–5 are capable of measuring and recording the investigated gaseous emissions; only AQMS 5 oversees CO measurements. Moreso, field PM_2.5, and PM₁₀ observations were performed in AQMS 1 and 2, respectively. The instrumentations used for measuring the gaseous pollutants’ concentrations were in situ close coupled multi-gas analyzers for real-time and simultaneous detection of gaseous emissions with integrated sample drying and data validation. UV fluorescence was used for detection of SO₂/H₂S analyzer, model AF-21 M, with maximum measuring figure of 2000 ± 1.0 µg/m³, where chemiluminescence NO₂ analyzer of model AC-32 M has a maximum measuring domain of up to 2000 ± 0.5 µg/m³. For O₃ analysis, a UV Photometric detection of model O₃-42 M has maximum measuring range of 500 ± 0.5 µg/m³. NDIR CO-12 M analyzer model has CO detection up to 36.0 ± 0.02 g/m³. Beta Gauge analyzer was used for PM detection of particulate diameter within the range of 0.01–20 microns, with potential particulate account of 9.5 ± 35 × 10⁴ PM_2.5/cm³ and 1.7 ± 60 × 10⁵ PM₁₀/cm³. For detection of the meteorological parameters (wind speed and direction, temperature, and humidity), a meteorological station equipped with model Lastem was put into service. These instrumentations were delivered by Environment SA manufacturer (France).

Outdoor air quality standards

Harmful outdoor air pollutants are categorized as gaseous sulfur derivatives (like dioxide [SO₂] and hydride [H₂S]), nitrogen dioxides ([NO₂], Ozone [O₃], and carbon mono-oxide [CO]), and suspended particulate matters, which contaminate the anthropogenic and biogenic activities in the industrial regimes associated with population (Al-Zboon & Forton, 2019; Lou et al., 2019; Ramli et al., 2020; Sharma et al., 2020). A variety of industrial applications are responsible for SO₂ and H₂S emissions, which mainly originate from desulfurization of crude oils to immobilize the sulfur contents from end-fuel derivates to other components like SO₂ and H₂S; therefore, strict precautions must be met during the design and operation of Amine Washing units to reduce the emission of SO₂, NO₂, and CO into outdoor air (Al Rabadi, 2021; Al Rabadi et al., 2018; Heeres & Rineksa, 2014). High sulfur-containing fuels produce SO₂ and H₂S in the industrial applications of energy production from integrated smokestacks, while NO₂ emissions are produced from denitrification processes of high nitrogen–containing fuel derivatives, thermal power plants, coal burning, and other industrial applications. Thus, NO₂ could be produced by the chemical reaction of free atmospheric oxygen and nitrogen, which is achieved under specific circumstances during combustion (Munsif et al., 2021). Furthermore, CO emissions are associated with fuel-fired boilers and incomplete combustion source in a steep rate, while exposure to SO₂, H₂S, and CO can play a vital role in asthma, bronchial symptoms, lung inflammation, and reduced functionality. Conversely, photochemical reaction produces several secondary pollutants from O₃ precursors like NO₂, SO₂, and CO (Hassan et al., 2019; Rovira et al., 2021), and elevated levels of atmospheric O₃ have been considered a major concern in the damage to materials and manufactured goods or negative drawbacks on crops and vegetation (Munsif et al., 2021). Exposure to excessive atmospheric O₃ is a major coincidence for asthma morbidity and mortality. Obviously, O₃ obstacle is oft experienced in large cities near industrial sectors (Hassan et al., 2013, 2019; EEA, 2021; WHO, 2021), and Yanbu is analogous to many other cities in the Arabian Peninsula and worldwide suffering from urban smog problems (Alharbi et al., 2015; Weng et al., 2014). Atmospheric O₃ may form hydroxyl radicals due to its radioactive force under specific conditions (Al-Harbi et al., 2020; Hassan et al., 2019). Meanwhile, NO₂ and nitric oxide (NO_x), SO₂, and CO are usually major O₃ precursors (Al-Azmi et al., 2009; Al-Jeelani, 2014; Butenhoff et al., 2015; Hassan et al., 2019; Shareef et al., 2018). The main parameters that steer O₃ formation are the ratio of the relevant atmospheric precursors and the intensity of solar radiation. Another related emission is the particulate matter (PM), which is classified as a common indicator of outdoor air pollution. It is produced from a complex mixture of solid and liquid particulates constituted from organic and inorganic components and suspended in air (atmospheric aerosols, various oxides, carbonates, sulfate and nitrate salts, black carbon [CS], metalloids, and Sahara Desert or the so-called mineral dust) (Hariri et al., 2013). While particulates with a diameter of 10 microns or less, PM₁₀, can penetrate and precipitate in respiratory vessels deep within the lungs, the more significant health-damaging particulates are those with a diameter of 2.5 microns or less, (PM_2.5) (Algarni et al., 2021; Dasari et al., 2020; Habeebullah, 2013; Modaihsh et al., 2015; Munir et al., 2013, 2016; Sharma et al., 2020; Ukhov et al., 2020). As a synopsis, these studies suggest that particle size is the main factor for determining the residence time of particles in the atmosphere. As an illustration, fine particles with a diameter of 2.5 microns or less can typically remain in the air for some days (Aina et al., 2014). The atmospheric half-life of coarse particles, however, can range from some minutes to several hours (Munir et al., 2016), due to the gravitational settling and the temperature inversions between the upper and lower atmospheric layers. By monitoring air pollution levels in the relevant industrial sectors to ensure they are within regional and/or international thresholds, domestic authorities could contribute, to a great extent, in reducing the burden of diverse cardiovascular and respiratory diseases as well as cancers (Alharbi et al., 2014, 2015; Al-Harbi et al., 2020; Almetwally et al., 2020).

Thresholds for outdoor emissions are regulated by custodial agencies: international bodies such as World Health Organization (WHO), and the European Environment Agency (EEA), and regional bodies like the Royal Commission for Jubail and Yanbu (RCY). Air quality measurements are typically reported in terms of mean daily concentrations classified as exposure times of 1, 8, or 24 h and annual mean concentrations. The gaseous accumulations into atmosphere were attributed to tremendous demand for manufactured goods and products without giving any focus to their environmental influence on Yanbu industry locality. As listed in Table 2, air quality limitations were summarized according to regional guidelines such as the RCY as well as those by international associations, namely, WHO and EEA. Routine air quality measurements are typically described in units of micrograms per cubic meter of air volume for all the investigated air emissions, except for CO, where it is expressed in units of grams per cubic meter of air volume. Reported guideline values for SO₂ according to RCY were 80 µg/m³ (annual mean), 367 µg/m³ (24-h mean), and a maximum of 734 µg/m³ (1-h mean), while a lower value of 40 µg/m³ (24-h mean) was reported by WHO. For H₂S, some flexibility in the typical values was tabulated by the RCY as follows: 24 µg/m³ (24-h mean) and 200 µg/m³ (1-h mean) to be aligned with environmental issues and the investors’ interests in the petrochemical industry. According to RCY, NO₂ guideline values are 94 μg/m³ (annual mean) and 658 μg/m³ (1-h mean), and since NO₂ is the main source of nitrate aerosols that constitute atmospheric O₃ and is linked to reduced lung functionality in residentials, strict limitations of 10 μg/m³ (annual mean) and 25 μg/m³ (24-h mean) were set by WHO, while moderate restrictions of 100 μg/m³ and 188 μg/m³ for annual and 24-h means, respectively, were reported by the EEA. For O₃, conventional thresholds are 240 μg/m³ (8-h mean) and a maximum of 160 μg/m³ (1-h mean) based on peak season, which is defined as an average of daily maximum 1 h of mean O₃ concentration. In this context, these O₃ thresholds were considered for ground-level O₃ and must not be confused with O₃ content in the upper atmosphere. Ground-level O₃ is produced by the photochemical reaction of O₃ precursors like NO₂, SO₂, and CO. Therefore, significantly lower levels of O₃ concentrations of 100 and 70 μg/m³ (8-h mean) have been established by the WHO and EEA, respectively; thus, excessive O₃ in outdoor air may have a marked impact on public health and serious lung diseases for residents in Europe and the USA. Furthermore, due to the absence of regional guidelines for CO emissions in the outdoor air, the international regulations by WHO and EEA were followed for the current investigations. The WHO found CO limitations of 4 g/m³ for 24-h mean, while the EEA found 9 g/m³ and 35 g/m³ for 8-h and 1-h means, respectively. PM_2.5 could be a constitutional fraction of PM₁₀; therefore, restricted limitations on its concentration were tabulated in comparison with coarser PM₁₀. In accordance to RCY, PM_2.5 guidelines are 15 g/m³ and 65 μg/m³ for annual and 24-h means, respectively. For WHO, restricted levels are 5 and 15 μg/m³ of annual and 24-h means for a comprehensive assessment of PM_2.5 outdoor air quality world widely. According to the EEA, moderate PM_2.5 limits of 12 g/m³ and 35 μg/m³ for annual and 24-h means were reported; meanwhile, PM₁₀ guidelines were stated by the regional RCY as 50 g/m³ and 150 μg/m³ for annual and 24-h means, respectively, and low levels were categorized by WHO as 15 g/m³ and 45 μg/m³ for annual and 24-h means, respectively, and the EEA set levels at 30 g/m³ and 150 μg/m³ for annual and 24-h means, respectively.

Table 2.

Ambient air quality thresholds

Emission			RCYe	WHOe	EEAe
SO2	[µg/m3]	Annual mean 24-h meana 1-h meanc	80 367 734	– 40 –	– – 200
H2S	[µg/m3]	24-h meana 1-h meanc	24 200	– –	– –
NO2	[µg/m3]	Annual mean 24-h meana 1-h meanc	94 – 658	10 25 –	100 – 188
O3	[µg/m3]	8-h meand 1-h meanc	240 160	100	70
CO	[g/m3]	24-h meana 8-h meand 1-h meanc	– – –	4 – –	– 9 35
PM2.5	[µg/m3]	Annual mean 24-h meana	15 65	5 15	12 35
PM10	[µg/m3]	Annual mean 24-h meana	50 150	15 45	30 150b

^aDefined as 99th percentile of the daily distribution of 24-h average concentrations

^bDefined as 98th percentile of the daily distribution of 1-h average concentrations

^cDefined as 99th percentile of the daily distribution of 1-h average concentrations

^dDefined as daily maximum distributed over 8-h average concentration

^eAccording to EEA, the time distribution is considered over a period of 3 years

The conversion factors were approximated according to the linear equation (EEA, 2021; WHO, 2021):

$$Conc.\left[\frac{\mu g}{m^3}\right]=Conc.\left[\frac{\mu g}{m^3}\right]·(12.187·\frac{{\mathit{Mwt}}_{\mathit{Gas}}}{T_{\mathit{abs}}[K]})$$ 1

Based on the pollution levels related to health consequences, an air quality index (AQI) is used to convey to the public of how healthy or unhealthy the outdoor quality is on a particular day. The AQI is used to evaluate if the locality’s air quality is better or worse in vicinities remote from the observation/measuring location or to relate measured concentrations to probable health implications. For each air pollutant concentration, in details, a scale of exceedances’ account with respect to thresholds would justify the particular air quality. An AQI between 0 and 50 is deemed “good,” 51 to 100 is “moderately polluted,” 101 to 150 is “unhealthy for sensitive groups,” 151 and 300 is “unhealthy,” 201–300 is “very unhealthy,” and over 300 is “hazardous.”

Results and discussion

Meteorological data

Figure 2 shows the rose diagram depicting meteorological data from the Yanbu Department of Meteorology. It depicts the average wind speed and direction for the entire period of 12 months and the entire 24-h period over the year 2015. Accordingly, the variation in the meteorological parameters has been widely studied in the Yanbu region and surroundings (Al-Zboon et al., 2022; Khalil et al., 2016), and based on the hourly observations, it was stated that wind speed and direction in Yanbu industrial complex is largely affected by the climate of the Red sea. Low wind speed in the Yanbu region alters dispersion of pollutants, and air pollution lasts long in the city vicinity. Analysis of meteorological data revealed that the average wind speed remains between 2.1 m/s and 3.0 m/s (3.9–5.8) knots. The wind over the entire investigated case study area was classified as calm, or the so-called light breeze, and was unchanged for the purpose of air quality assessment. Furthermore, the meteorological data were presumed to be unchanged for the years 2015–2020, and the net wind direction was determined to be north within the offshore zone and north-west within the onshore zone (Red Sea’s eastern coasts).

Fig. 2.

Average wind speed and direction of Yanbu industrial complex and the residential area

Also, analogous findings were highlighted in related studies (Hariri et al., 2013; Hassan et al., 2013; Munir et al., 2016). The alternation of weather conditions on the east coast of the Red Sea has the following sequence: the spring season begins from early March till June, while summer term begins from June to August/September. From March to September, the hot month period is defined by an increase in temperature and ambient humidity, resulting in elevated coastal conditions (Aina et al., 2014; Al-Jeelani, 2014; Dasari et al., 2020; Habeebullah, 2013; Hariri et al., 2013; Hassan et al., 2013, 2019; Khalil et al., 2016). Afterward, the autumn season starts from the end of September to November when the weather changes from elevated coastal conditions to mild status of lower temperature and humidity rates. Due to the promotion of seawater evaporation, the maximum ground temperature during the warmest months could reach average highs around 45 °C with a relative humidity of more than 60%, while the mild status of the region is characterized by moderate temperatures of about 30 °C and relative humidity of 30%. In literature, the key climatic factors that potentially affect air quality are temperature, atmospheric visibility related to air humidity and cloud fraction, and horizontal wind speed and its direction. According to relevant studies by Al-Jeelani (2014) and Khalil et al. (2016), the variations in meteorological parameters in KSA’s coastal areas throughout the year may have a less pronounced contribution on the concentrations’ alternation of air pollutants than those caused by existing industrial infrastructures and other associated anthropogenic activities. According to these findings, the temporal and spatial fluctuation of air quality pollutants is related to the temperature effect derived from the temporal alternation of air pollutants’ concentrations between day and night as well as among different seasons.

Ambient air quality of gaseous pollutants

The average measurements of air emissions include the major outdoor air pollutants, SO₂, H₂S, NO₂, O₃, CO, PM_2.5, and PM₁₀ for the five AQMSs, and the time intervals of the relevant gaseous emissions were based on continuous diurnal and night measurements covering 24 h per day, over the calendar year duration of 12 months, and through the calendar year period from 2015 to 2020. These measurements intrinsically include the variation of day/night periods, with the influence of the temperature alternation between the hot and the cold months as well as the spatial distribution among the five foreseen AQMSs.

Daily SO₂

The average daily SO₂ measures for all AQMSs are illustrated in Fig. 3a–e, respectively, and as indicated, the detected concentrations SO₂ were obtained for all foreseen temporal and spatial intervals. Obviously, a maximum peak in daily SO₂ concentration profiles of around 35 μg/m³ is obtained during the midday periods between 09:00 A.M. and 03:00 P.M. (local Yanbu timing) for all years and spatial AQMSs, then, approaches lower limits during other day periods for the same relevant year around below 10.0 μg/m³, which is consistent with the findings by previous studies (Al-Harbi et al., 2020; Al-Zboon et al., 2022; Dasari et al., 2020; Hassan et al., 2019; Hindy & Abdelmaksoud, 2016; Khalil et al., 2016; Munir et al., 2013, 2016). The maximum peak is attributed for processing high sulfur content in fuel derivates, especially in the heavy fuel oil, and due to the high industrial activities within Yanbu industrial complex during the midday period. Therefore, other maximum daily SO₂ concentrations were obtained to reach the values of 70.0 μg/m³ after the peak period around 6:00 P.M. (local Yanbu timing) for AQMS 2 in 2015. Principally, these higher SO₂ measurements could be attributed to extraordinary industrial activities in the cement industry during that time interval. To pay more attention on this finding, an analysis of the field measurements is considered for the year 2015 and all AQMSs at 10:00 A.M. (local Yanbu timing) as an instance. Different figures were attained as 7.5 μg/m³ for AQMS1, 29.0 μg/m³ for AQMS2, 8.3 μg/m³ for AQMS3, 22.5 μg/m³ for AQM4, and 18.1 μg/m³ for AQMS5. This spatial variation is attributed to the nearby source from the relevant AQMS. More focus is to be drawn on the results of AQMS 5, before and after the daily peak measurements (9:00 AM till 3:00 PM); the lower limits achieved a high range of daily SO₂ measurements between 15.0 and 25.0 μg/m³, especially for the periods of 2016 till 2018. This observation might be explained by a combination of covariables that could be responsible for such elevated measurements outside of peak times, since AQMS 5 is in the middle of the Yanbu industrial complex close to extremely active industrial facilities and a power plant. Even if the elevated SO₂ concentration were obtained, the daily emissions were below the thresholds (Table 2). The obtained measurements indicated that SO₂ is not an important contaminant of concern in the outdoor air of this specific location. Conversely, there is evidence for applying abatement policies to reduce the gaseous emissions toward the residential area.

Fig. 3.

Average daily measurements of SO₂ concentrations within the period of 2015–2020: a for AQMS 1, b AQMS 2, c AQMS 3, d AQMS 4, and e AQMS 5

Monthly SO₂

The average monthly SO₂ measurements for all AQMSs are shown in Fig. 4a–e, respectively. Generally, it can be deduced from these diagrams that the temporal variation in SO₂ mean concentrations differs between the cold (November to March) and hot months (April to October). During the hot months, lower emissions were recorded in the range of 10 to 20 μg/m³, while higher emissions were obtained during the cold months of the year in the range of 30 to 45 μg/m, which were even below the allowable limitations. In previous studies, the effect of ground temperature was distinguished from spatial distance for the emission source and other meteorological parameters such as humidity (Aleem, 2013; Munir et al., 2013; Al-Jeelani, 2014; Khalil et al., 2016; Ukhov et al., 2020). This temporal variation in monthly SO₂ measurements was attributed to extraordinary energy requirements for heating the industrial and residential premises during cold months, and current findings particularly agree with those from the study by Al-Jeelani (2014). For illustration, the spatial distribution is deduced by comparing SO₂ concentrations in October 2015 for all AQMSs. Figures attained as following 10.5 μg/m³ for AQMS1, 55.3 μg/m³ for AQMS2, 13.4 μg/m³ for AQMS3, 23.2 μg/m³ for AQMS4, and 13.7 μg/m³ for AQMS5. AQMS 2 recorded an elevated SO₂ concentration due to huge demands in cement industry during this period, while records of AQMS 4 could be contributed to thermal demands for nearby light industry and Yanbu vicinity residential. According to the thresholds in Table 2, the salient result generally showed an obvious consistency in the average monthly SO₂ emissions to be around the acceptable limits.

Fig. 4.

Average monthly measurements of SO₂ concentrations within the period of 2015–2020: a for AQMS 1, b AQMS 2, c AQMS 3, d AQMS 4, and e AQMS 5

Annual SO₂

Average annual SO₂ measurements are summarized for the five monitoring AQMSs in Fig. 5. The annual trends were based on the continuous measurements using data collected over the entire period of 2015–2020, and the assessment (represented by the statistical criteria of mean and account of exceedances that SO₂ measurements violate RCY limitations) was conducted to determine annual SO₂ trends. Statistical data were highlighted to account for the number of exceedances when the gaseous measurements of air pollutants studied in current investigations only violate the RCY guidelines, considering the geographical location of the case study as well as the comprehensiveness of the RCY guidelines for all the investigated air pollutants in comparison to the restrictive WHO and EEA guidelines that are widely applied in Europe and over the Pacific regions. Except that average SO₂ concentrations were obtained, e.g., for AQMS 2 as 28.4 μg/m³ in 2015, 20.2 μg/m³ in 2016, 19.5 μg/m³ in 2017, 18.9 μg/m³ in 2018, 15.6 μg/m³ in 2019, and 5.8 μg/m³ in 2020, respectively. The obtained trend for annual SO₂ mean concentration for all the monitoring AQMSs showed that there is a decrease in the annual SO₂ concentration over the investigated periods from 2015 to 2020. Some exceedances were routinely recorded behind the relevant tabulated guidelines by the RCY (80 μg/m³). Extensively, few exceedances were recorded in AQMS 2 such that 8 violations were recorded in 2015, 7 in 2016 and 2017, 6 in 2018, 3 in 2019, and 2 in 2020. Obviously, a pronounced decrease in annual SO² mean concentration was generally observed through the period from 2015 to the beginning of the first COVID-19 lockdown in 2020. Generally, reduced accounts were documented of the annual exceedances for SO₂ measurements during the period from 2015 to 2020 for AQMS 2–5, respectively. By accounting for the number of violations of annual SO₂ concentration for all AQMSs, which is below 50 on AQI scale, the pollution levels related to health consequences are deemed as good. Whereas potential health hazards due to the inhalation of SO₂ gas could lead to nose and throat infection, bronchitis, and eyes irritation, high dosages or prolonged exposure to SO₂ might impact a serious lung malfunction, eventually leading to death. Therefore, symptoms may vary according to exposure time and the residential’s sensitivity, and in most cases, the symptoms appear within short periods of 10–15 min after exposure. According to EEA (2021), WHO (2021), and RCY (2022), it has been reported that at up to a concentration of 100 µg/m³, the bronchi would be significantly affected, while higher concentrations of up to 400 µg/m³ reduce lung functionality. Considering the reduced accounts of the annual exceedances for SO₂ measurements in spatially distinct locations, it was concluded that the Yanbu industrial complex was subject to lower consequence levels of SO₂ accumulation without significant threats to the environment and public health.

Fig. 5.

Spatial average annual SO₂ measurements for AQMSs during 2015–2020, with account of exceedances per annum that SO₂ measurements violate RCY limitations

Daily H₂S

The trends of H₂S daily measurements for all AQMSs are shown in Fig. 6a–e. In general, a peak was observed for all AQMSs between 8:00 A.M. and 3:00 P.M. (local Yanbu timing), with a maximum H₂S concentration of 12.4 μg/m³, and a minimum H₂S concentration of ≤5.0 μg/m³ was recorded from early morning to evening for all AQMSs. This trend can be mainly attributed to anthropogenic and industrial activities during the day. Nonetheless, the following exceptions were also observed: a maximum concentration of approximately 122.0 μg/m³ for AQMS 2 in 2017, 40.0 μg/m³ for AQMS 3 in 2015, and 28.0 μg/m³ in 2016, although this peak was recorded around 8:00 P.M. (local Yanbu timing), which was attributed to industrial activities during this period. Despite the maximum concentration obtained for AQMS 2 in 2017, there were no exceedances according to the RCY limitations of 200 μg/m³ (1 h mean). Additionally, the spatial distribution for all AQMS 2–5 can be highlighted by analyzing the daily H₂S concentration at 08:00 A.M. (local Yanbu time) for e.g. 2017, where figures were found as 8.1 μg/m³, 15.7 μg/m³, 3.8 μg/m³, 3.2 μg/m³, and 16.2 μg/m³, respectively, different concentrations were obviously obtained. Additionally, the trend for AQMS 5 varied, where a peak (38.0 μg/m³) was observed between 8:00 A.M. and 11:00 A.M. (local Yanbu timing), recording some violations in the daily concentration with respect to threshold set by RCY (24 μg/m³). However, the daily H₂S concentration dropped below the threshold set by RCY for the rest of the day. This could be attributed to the geographical location of AQMS 5 that was located at the center of on-shore, industrial, and anthropogenic activities. The daily H₂S concentration varies within the acceptable limit and emphasized the efficient policies applied to abate the gaseous emissions from the smokestacks of the manufacturing facilities neighboring AQMS 5.

Fig. 6.

Average daily measurements of H₂S concentrations during 2015–2020 for a AQMS 1, b AQMS 2, c AQMS 3, d AQMS 4, and e AQMS 5

Monthly H₂S

The experimental data for the monthly H₂S concentrations for all AQMSs are presented in Fig. 7a–e. In situ H₂S emissions revealed a pattern, where emissions increased during the spring and then decreased during the hot months. In contrast, monthly H₂S concentrations peaked during autumn, and low concentrations were recorded during the winter season. Even with pertinent studies carried out in other urban environments in the Gulf region (Alharbi et al., 2014), the Mediterranean region in Egypt (Hindy & Abdelmaksoud, 2016), Spain (Rovira et al., 2021), and Greece (Tsilingiridis et al., 2002), the lowest monthly H₂S concentrations recorded in winter may be related to the absence of dusty winds, which were analogous in other urban Saudi cities such as NEOM (Dasari et al., 2020). Contrary to the findings of this study, the buildup in monthly H₂S concentrations was attributed to increased demands for fuel derivatives for heating during winters. By comparing the present results with the thresholds stated by RCY, we concluded that Yanbu city was subjected to unharmful levels of H₂S during 2015–2020. Apart from natural phenomena, such as dusty winds in summer, the turbulence of the boundary layer was less in springs and winters and trapped the pollutants near the ground because of fewer temperature inversions between the upper and lower atmospheric layers. During the summer months, in contrast, thermal heating at the ground level promoted turbulence and the mixing of atmospheric layers, which, consequently, enhanced buoyancy and the migration of outdoor pollutants toward upper atmospheric layers. In addition, this suggested that H₂S emissions were adequately regulated through proper distribution of monitoring AQMSs over the Yanbu industrial complex, considering that H₂S emissions were a genuine environmental hazard that degrade the quality of life and endanger public health in both residential and industrial areas.

Fig. 7.

Average monthly measurements of H₂S concentrations during 2015–2020 for a AQMS 1, b AQMS 2, c AQMS 3, d AQMS 4, and e AQMS 5

Annual H₂S

Average annual H₂S concentrations are summarized for the five monitoring AQMSs in Fig. 8. Annual trends in H₂S concentration were based on continuous measurements using the data collected during 2015–2020, and the assessment, represented by the statistical parameters of mean and account of exceedances, indicated that NO₂ measurements violated RCY limitations. The average annual H₂S concentrations decreased from 69.4 μg/m³ in 2015 to 30.1 μg/m³ in 2020 for AQMS 1, from 6.2 μg/m³ in 2016 to < 3.0 μg/m³ in 2020 for AQMS 2, from 18.4 μg/m³ in 2015 to 1.6 μg/m³ in 2020 for AQMS 3, from 2.6 μg/m³ in 2015 to < 1.2 μg/m³ in 2020 for AQMS 4, and from 12.4 μg/m³ in 2015 to < 8.0 μg·/m³ in 2020 for AQMS 5. This alternating annual H₂S trend might be explained by the differences in the aerosol size in the source regions. These findings have been verified for other urban environments in the east coasts’ region of the Red Sea (Aleem, 2013; Alharbi et al., 2014; Dasari et al., 2020; Hariri et al., 2013; Khalil et al., 2016). The main anthropogenic cause of H₂S emissions over AQMSs 1, 2, and 4 was vehicular traffic density, as well as light and cement industries. Meanwhile, for AQMS 3 and 5, the trend in annual H₂S emissions can be explained by the massive energy demands of oil processing facilities for manufacturing fuel–based hydrocarbon derivatives. According to regulations, H₂S exhibits a wide range of potential human health impacts, with a gradual increase observed in the severity of symptoms, which depends on exposure periods. Exposure to 100 µg/m³ H₂S for 15–30 min alters breathing and causes drowsiness, which could be fatal after 48 h, and 200 µg/m³ of H₂S could cause conjunctivitis and respiratory tract irritation, with pulmonary edema occurring from prolonged exposure, resulting in death within 30–60 min. Eventually, we observed violations to the RCY guidelines in annual H₂S measurements. Additionally, 48 and 28 exceedances were recorded in 2015 and 2020, respectively, for AQMS 1; 43 and 21 exceedances were recorded in 2015 and 2020, respectively, for AQMS 2; 54 and 27 exceedances were recorded in 2015 and 2020, respectively, for AQMS 3; 44 and 20 exceedances were recorded in 2015 and 2020, respectively, for AQMS 4; and 55 and 22 exceedances were recorded in 2015 and 2020, respectively, for AQMS 5. By accounting for the number of violations of annual H₂S concentration for all AQMSs, which is in general below 50 on AQI scale, the pollution levels related to health consequences are deemed as good. An exception was noticed where the number of exceedances was recorded 55 for AQMS 5; hence, AQI was considered as moderately polluted. These violations were synchronizing the raise in production capacities in the nearby chemical facilities in 2015. In this context, a suitable program for limiting the detrimental effects of pollution on health during production scenarios with maximum capacities in the neighboring facilities could be recommended based on the current findings. In particular, the RCY received the results for excessive execution of the air quality improvement strategy at the industrial and nearby residential areas for this purpose.

Fig. 8.

Spatial average annual H₂S measurements for AQMSs during 2015–2020, with account of exceedances per annum that H₂S measurements violate RCY limitations

Daily NO₂

The daily profiles of NO₂ concentrations for all AQMSs are shown in Fig. 9a–e. The daily NO₂ concentration trend exhibited two peaks; the first observed around 8:00 A.M. (local Yanbu timing), reflecting a reduction in NO₂ concentrations to reach a minimum around 3:00 P.M. (local Yanbu timing). After midday, the profiles were reestablished with a second peak for evenings and a stable concentration during nights. This trend was symmetric with the findings of previous studies (Al-Azmi et al., 2009; Alharbi et al., 2014; Al-Jeelani, 2014; Butenhoff et al., 2015; Hassan et al., 2019; Shareef et al., 2018; Tsilingiridis et al., 2002). These concentrations highlighted the influence of photochemical processes favored by solar radiation for O₃ formation within the lower atmospheric layers. The photochemical reaction occurs in the following sequence: NO₂ in the outdoor air is broken down by sunlight to generate NO and free oxygen atoms, which then react rapidly with ambient O₂ to form O₃. This phase was responsible for the decrease in daily NO₂ concentrations during midday. In contrast, O₃ decomposition starts with its reaction with NO to complete the cycle, and hence, NO₂ is further generated during the rest of the day. This phase was accountable for the second peak in the daily profile of NO₂ concentration. The lowest levels were recorded during midday around 3:00 P.M. (local Yanbu timing), establishing a range of NO₂ daily concentrations between 5.0 and 10.0 µg/m³ for AQMS 1; 5.0 and 15.0 µg/m³ for AQMS 2; 28.0 and 30.0 µg/m³ for 2015–2017 and 12.0 and 17.0 µg/m³ for 2018–2020 for AQMS 3; 5.0 and 8.0 µg/m³ for all years for AQMS 4, except for 2019 that exhibited a minimum concentration of 15 µg/m³; and 5.0 and 15.0 µg/m³ for AQMS 5. The attained minimum was associated with the consumption of daily NO₂ deposition processes, which brought daily NO₂ concentrations to acceptable limits. Moreover, the minimum daily NO₂ concentration at AQMS 3 ranged between 28.0 and 30.0 µg/m³ for the years 2015–2017, compared to the range of 12.0–17.0 µg/m³ for the years 2018–2020. This ensured low NO₂ emissions in accordance with the RCY and EEA guidelines.

Fig. 9.

Average daily NO₂ concentrations during 2015–2020 for a AQMS 1, b AQMS 2, c AQMS 3, d AQMS 4, and e AQMS 5

Monthly NO₂

Figure 10a–e illustrate the monthly variation of NO₂ concentrations for all AQMSs. Similar to previous studies (Hariri et al., 2013; Hassan et al., 2013; Hassan et al., 2019; Al-Jeelani, 2014; Hindi & Abdelmaksoud, 2016; Khalil et al., 2016; Dasari et al., 2020), there was a direct association between meteorological conditions, such as ambient temperature, and in situ NO₂ concentrations. The low monthly NO₂ concentrations during midday might be due to intense dilution conditions under the influence of temperature inversions. Furthermore, elevated temperatures and dense solar radiation during midday promoted photochemical reactions, which enhanced the chemical degradation of NO₂, reducing their in situ accumulation. Nonetheless, the following trend was noticed: maxima were recorded during winter 2018 for AQMS 1, with concentrations reaching >90 µg/m³; for AQMS 2 with 35.4 µg/m³; for AQMS 30.5 µg/m³; for AQMS 4 32.5 µg/m³; and for AQMS 5 60.3 µg/m³. The trend of monthly NO₂ concentrations was attributed to cross-acting phenomena, such as the alternation in thermal boundary-layer mixing and anthropogenic emissions, in combination with geographical location from the source regions.

Fig. 10.

Average monthly NO₂ concentrations during 2015–2020 for a AQMS 1, b AQMS 2, c AQMS 3, d AQMS 4, and e AQMS 5

Annual NO₂

Average annual NO₂ measurements for the five AQMSs are summarized in Fig. 11. The annual NO₂ trends were based on the continuous measurements over the entire period of 2015–2020, and the assessment, represented by the statistical parameters of mean, and account of exceedances that NO₂ measurements violated the RCY limitations, was used to determine annual NO₂ trends. The findings of the spatiotemporal analysis for NO₂ emissions revealed a general trend, whereby the concentrations fluctuate with each successive year and are kept below the thresholds. This trend focused on the effective control of the anthropogenic and industrial activities in the Yanbu industrial complex. The basic human sources for NO₂ emissions were automotive emissions, in addition to emissions from smokestacks in chemical and oil processing facilities. Yanbu is also characterized by the highest number of vehicle proprietorships, due to absence of public transportation such as buses, trains, and street-trams. In addition, Yanbu’s sprawling residential areas, apart from the industrial complex, have massively contributed to outdoor air emissions through daily driving routes from/to work. This fact has been highlighted by previous studies (Alharbi et al., 2014; Al-Jeelani, 2014; Butenhoff et al., 2015; Shareef et al., 2018), where it was noted that NO₂ concentrations in populated suburbs might result from vehicle emissions and urban activities, whereas industrial emissions might be caused by hydrocarbon production and processing activities.

Fig. 11.

Spatial average annual NO₂ measurements for AQMSs during 2015–2020, with account of exceedances per annum that NO₂ measurements violate RCY limitations

Furthermore, the spatiotemporal variability in NO₂ concentration was combined with the alternation of in situ measurements of other pollutants, such as atmospheric O₃, due to photochemical reactions and deposition. Owing to non-linear O₃ precursor alliance, a mitigation plan for atmospheric precursors, such as NO₂, might not abate O₃ formation in the case of abundant solar radiation. Moreover, a potential increase might occur in O₃ and NO₂ concentrations in the background, which might be highly dependent on the surrounding meteorology and local conditions, as has been stated by several studies (Khalil et al., 2016; Munir et al., 2013, 2016; Rovira et al., 2021). For an effective mitigation plan, a comprehensiveness O₃–NO₂ relationship along with the meteorological measurements is required. Additionally, potential human health impacts of NO₂ exposure must be further addressed; short-term exposure for a period of 1 h below 200 µg/m³ is associated with adverse health effects such as bronchi resistance, forced expiratory volume, and carbachol reactivity. High NO₂ concentrations could cause serious functional issues in patients with chronic obstructive pulmonary disease. Additionally, long-term exposure for a period of 24 h below 40 µg/m³ is associated with respiratory symptoms/diseases, hospital admissions, otitis media, and even mortality. Regularly, exceedances were accomplished under circumstances that were acceptable. Although annual in situ NO₂ measurements were illuminated by sustainable management policies, it must be emphasized that exceedances were still attained. Despite this, the AQI was deemed to be good.

Daily O₃

Th spatiotemporal variations in daily O₃ concentrations for all AQMSs are described in Fig. 12a–e. The daily O₃ measurements (over 24 h) indicated lower concentrations during the night and early morning. From 9:00A.M. (local Yanbu time) onward, O₃ concentrations would peak until midday. Afterward, minimal concentrations were recorded at the end of day, as well as during evenings and nights. This trend could be explained by high solar intensity during midday. As shown in Fig. 9a–e, the daily O₃ profiles aligned with the profiles of daily NO₂ concentrations, with the first peak synchronously obtained for NO₂ emissions at the time when low daily O₃ concentrations were recorded. Meanwhile, when daily NO₂ concentrations were low, we noticed high O₃ concentration. These findings indicate that the variability in daily O₃ concentrations can be explained by a negative linear dependence on O₃ precursor’s concentrations, which has been addressed in studies conducted by Tsilingiridis et al. (2002), Habeebullah (2013), Munir et al. (2013), Munir et al. (2016), Al-Jeelani (2014), Butenhoff et al. (2015), Shareef et al. (2018), Hassan et al. (2013), Hassan et al. (2019), and Al-Harbi et al. (2020). As deduced from these studies, NO₂ and volatile organic compounds (VOCs) combine in the presence of sunlight to produce the reactive gas O₃. There are many different sources that release these contaminants, such as industrial emissions, automobile exhaust, and more. When there is intense solar radiation and it is warmer outside, the O₃ amount in the surrounding air is usually at highest level. For illustration, the daily O₃ concentration for 2019 at midday was recorded as the following 58.2 µg/m³, 33.5 µg/m³, 43.6 µg/m³, 29.8 µg/m³, and 57.8 µg/m³ for AQMS 2–5, respectively. This spatial variation in the daily O₃ concentration is attributed to topographical features, as well as the influence of meteorological parameters, such as humidity and thermal boundary layer mixing, on daily O₃ concentrations. Evaluation of the outdoor air quality of the study area according to the regulatory standards of EEA for the protection of human health of 70.0 µg/m³ confirmed that only on some occasions, in situ O₃ levels violated these regulations, but otherwise, regulated limitations of the RCY and WHO guidelines were met. The local authorities may implement measures to lessen NO₂ and VOC emissions during daily peak periods, such as restricting vehicle use or cutting back on industrial activity during peak hours. There are numerous actions people can be taken in addition to abovementioned ones to lower the daily O₃ concentration in Yanbu Industrial City. Use low-VOC products in the house, take public transportation instead of driving alone, and turn off lights and devices when not in use to save energy.

Fig. 12.

Average daily O₃ concentrations during 2015–2020 for a AQMS 1, b AQMS 2, c AQMS 3, d AQMS 4, and e AQMS 5

Monthly O₃

Figure 13a–e illustrate average monthly O₃ measurements for all AQMSs during 2015–2020. The climatological status was considered for the Yanbu industrial complex and the surrounding residential areas, with the alternation of the cold and warm months throughout the year. The monthly O₃ concentrations vary monthly based on several variables, such as climate, emissions from nearby sources, and seasonal variations. The monthly concentrations are usually higher during the summer when there is more sunlight and temperatures are higher. This is because, under these circumstances, the chemical reactions that produce O₃ happen more rapidly. However, other co-variables like wind patterns and the location of emission sources can also contribute in the alternation of the monthly O₃ concentration. For clarification, the monthly O₃ concentrations revealed that there were slight increase in outdoor O₃ levels that established a peak; 105.8 μg/m³ during May 2017 for AQMS 1; 42.5 μg/m³ during June 2015 for AQMS 2; 37.9 μg/m³ during August 2016 for AQMS 3; 58.2 μg/m³ during July 2015 for AQMS 4; and 54.3 μg/m³ during April 2015 for AQMS 5. Due to unreliability of the experimental data, monthly O₃ concentrations were not considered for AQMS 3 during 2017. From 2015 to 2020, monthly O₃ concentrations were highest during the hot months (March to September). In contrast, lower O₃ levels were recorded during the cold months (October to February). These findings indicated the role of outdoor temperature, a vital meteorological parameter, in variations in O₃ concentrations in outdoor air because of its influence on photochemical processes that favored O₃ formation and accumulation within the lower atmosphere during midday. This finding was confirmed by previous studies (Habeebullah, 2013; Hassan et al., 2013, 2019). However, few differences in monthly O₃ concentrations were noticed with respect to the general behavior for all AQMSs, except that for AQMS 5, which could be attributed to the enormous industrial activities within oil processing facilities and huge exportation through nearby terminals during the hot months of the year. Nonetheless, on certain occasions, exceedances in monthly O₃ concentrations occurred in accordance with the RCY, WHO, and EEA guidelines. Despite the Yanbu industrial complex making progress in recent years in terms of reducing emissions and improving air quality, there is still work to be done to guarantee that arrangements are made to ensure that the monthly O₃ concentration and other pollutants remain within acceptable boundaries for the health and well-being of inhabitants as well as the environment. Monitoring air quality and taking action to reduce emissions can help the community progress toward a healthier and cleaner future.

Fig. 13.

Average monthly O₃ concentrations during 2015–2020 for a AQMS 1, b AQMS 2, c AQMS 3, d AQMS 4, and e AQMS 5

Annual O₃

Average annual O₃ measurements are summarized for all AQMSs in Fig. 14. Annual O₃ profiles were based on continuous measurements collected over the period 2015–2020, and the assessment, represented by the statistical parameters of mean, and account of exceedances that O₃ measurements violated the RCY limitations, was used to assess annual O₃ profiles and examine the outdoor air quality. The spatiotemporal analysis of O₃ emissions revealed a trend in which these concentrations appeared to change with each year: 30.3 µg/m³ in 2015 to 12.8 µg/m³ in 2020 for AQMS 1; 30.3 µg/m³ in 2015 to 12.8 µg/m³ in 2020 for AQMS 2; 31.1 µg/m³ in 2015 to 27.1 µg/m³ in 2020 for AQMS 3; 43.1 µg/m³ in 2015 to 35 µg/m³ in 2020 for AQMS 4; and 29.3 µg/m³ in 2015 to 37.5 µg/m³ in 2020 for AQMS 5. This trend could be attributed to effective regulations on anthropogenic and industrial activities in the Yanbu complex with residential areas. According to previous findings (Butenhoff et al., 2015; Khalil et al., 2016; Shareef et al., 2018), outdoor temperature, as the dominant meteorological parameter, was responsible for O₃ formation and accumulation in the lower atmosphere as it enhanced photochemical reactions and air convective currents at the ground level. The study’s findings indicate that Yanbu Industrial City’s yearly ozone concentration should raise concerns because it may be harmful to people’s health, particularly respiratory condition sufferers. For the potential health impacts, it has been reported up to O₃ concentration of 100 μg/m³, and mortality increases significantly at an exposure time of 8 h. Low concentrations might lead to irritation in the respiratory system, breathing restrictions, and massive lung malfunction, and eventually, permanent damage to the lungs. Based on current assessment, some violations of the tabulated guidelines were observed over the study period, in addition to a trend where low O₃ exceedances were recorded with each successive year. Most of the in situ measurements were attained below the threshold of 240 µg/m³ over 8-h exposure periods. During congestion periods, O₃ concentrations in the case study area might reach elevated figures. Because the percentage of exceedances was under 50, the AQI was regarded as being good. However, O₃ concentrations in the case study area could achieve higher figures during periods of congestion. Reducing emissions from automobile exhaust and industrial smokestacks is such recommendation forwarded to the RCY to overcome this obstacle.

Fig. 14.

Spatial average annual O₃ measurements for AQMSs during 2015–2020, with account of exceedances per annum that O₃ measurements violate RCY limitations

Daily CO

The average CO daily measurements are diagrammed in Fig. 15a, b, respectively, through the years 2015–2020 for the investigated AQMSs 2 and 5. While, the experimental results for the other investigated monitoring AQMSs 1, 3, and 4 were unreliable and were excluded from the current assessment. As highlighted, CO accumulations were produced directly from smokestacks because of the incomplete combustion of hydrocarbon derivatives. Consequently, power plants and refineries were classified as rich sources of CO emissions according to WHO and EEA tabulations. According to related studies (Butenhoff et al., 2015; Dasari et al., 2020; Habeebullah, 2013; Munir et al., 2013, 2016), CO directly contributes to photolysis reaction as an O₃ precursor, reacting with ambient O₂ to form O₃ and carbon dioxide as a by-product, aided by dense solar radiation. Conversely, CO participates in oxidation–reduction reaction for ambient humidity and reacts with the NO contaminant in lower atmosphere layers to form NO₂, which in turn contributes as an O₃ precursor through a photochemical reaction. Consequently, photolysis and oxidation–reduction reactions were stimulated at elevated temperatures because they could occur simultaneously. A maximum level in CO concentration was recorded with the value of 1.05 g/m³ for AQMS 2 and 0.9 g/m³ for AQMS 5. It was observed that the daily CO concentrations for AQMS 2 were a little erratic, while clear trends were attained in CO concentrations for AQMS 5, which obviously peaked around 8:00 A.M. (Yanbu local time). This interval normally synchronizes with the beginning of an ordinary labor day in Yanbu, as well as with the peaks in daily NO₂ concentrations. This could be evidence of the occurrence of oxidation–reduction reaction for ambient humidity. Furthermore, almost unchanged CO levels in the ambient were attained for the rest of the day. This finding was highlighted in previous studies by Munir et al. (2013) and Munir et al. (2016). Based on their findings, a combination of co-variables could contribute to almost unchanged CO levels beyond the peak periods. Climate, traffic patterns, and the manufacturing processes deployed by the nonstop industrial facilities contribute such covariables with a footprint on CO emissions. Minimizing your exposure to CO is crucial for your health, so as to avoid congested regions during rush hour, maintain the appliances and heating system appropriately, and heed any local air quality advisories or warnings.

Fig. 15.

Average daily measurements of CO concentrations in the period 2015–2020: a for AQMS 2 and b AQMS 5

Monthly CO

The monthly variation of CO concentrations is depicted in Fig. 16a, b for monitoring AQMSs 2 and 5 over the period 2015–2020. The experimental data for the other AQMSs 1, 3, and 4 were inaccurate and were not considered in the evaluation. March to the end of August is considered the period, including the spring and summer seasons, while September to the end of February are counted as cold months, including autumn and winter terms. In general, the monthly CO profile approached higher limits in cold months and lower values in warm months. By AQMS 2, a minimal CO concentration was recorded at 0.3 g/m³ in May and a peak value of 0.75 g/m³ in February. Hence, by AQMS 5, a minimal CO concentration was recorded at 0.21 g/m³ in June and a peak value of 1.3 g/m³ in December. A minimal CO concentration was recorded at 0.17 g/m³ in June and a peak value of 0.37 g/m³ in November. This pattern could be attributed to the most probable photochemical reaction due to the intense solar radiation, as well as the stronger vertical boundary layer mixing during the hot months, which causes reduced CO levels in the outdoor air. High concentrations were recorded in the cold months due to additional demands for hydrocarbon fuels for heating industrial and residential premises. Equivalent results were shown by previous studies focused on outdoor emissions for the eastern coastal region of the Red Sea (Al-Zboon et al., 2022; Dasari et al., 2020; Hariri et al., 2013; Munir et al., 2013, 2016). It ought to recognize that for authorities to adopt effective policies for decreasing CO pollution and preserving public health, it is essential to keep apprised of the spatial and temporal fluctuations in CO concentrations. By locating sections and periods that have elevated CO concentrations, targeted actions can be taken to mitigate emissions and exposure to this hazardous pollutant.

Fig. 16.

Average monthly measurements of CO concentrations within the period 2015–2020: a for AQMS 2 and b AQMS 5

Annual CO

The average CO annual measurements are summarized in Fig. 17 for the monitoring AQMSs of 2 and 5. The evaluation did not take into account the incorrect experimental data for the other AQMSs 1, 3, and 4. Annual CO profiles were based on the continuous measurements using data collected over the entire period of 2015–2020, and the assessment, represented by the statistical criteria and account of exceedances that CO measurements violate the WHO limitations, was assigned for the assessment of annual CO profiles. The findings of the spatiotemporal analysis for CO emissions have shown a general trend, and these concentrations obviously decrease with the years: 0.7 g/m³ in 2015 to 0.4 in 2020 for AQMS 2 and 0.8 g/m³ in 2015 to 0.4 in 2020 for AQMS 5. Again, this trend could be attributed to the effective adjustment in the anthropogenic and industrial activities in the Yanbu complex. Basically, the human sources for CO emissions could be intense traffic and heating demands of residential premises during the cold months, as also the industrial infrastructure. Potential impacts on public health are significant over CO exposure periods. Once CO gas penetrates the bronchi and eventually the lungs, it is readily absorbed by blood hemoglobin to form carboxyhemoglobin. Higher concentrations of carboxyhemoglobin in the blood channels are associated with significant health symptoms, such as permanent headache, dizziness, and nausea, which could cause coma and eventual death. To maintain safe CO levels, WHO and EEA restrict CO hazards to 4, 9, and 35 g/m³ over exposure periods of 24, 8, and 1 h, respectively. It was deduced, by comparing the mean annual CO concentrations with thresholds, that the account for exceedances of annual CO in situ measurements had decreased over the calendar years 2015 to 2020. The incomplete combustion of carbonaceous derivatives has a significant impact on CO levels within the Yanbu industrial complex and the residentials in the vicinity, and all in situ measurements were obtained below the reported levels. Despite the recorded violations in CO, annual profiles that are below 50, the AQI was judged to be good. Tracking of annual CO concentrations emphasizes on the identification of seasonal trends and patterns that can be used to guide air quality management strategies, thus lessening environmental deterioration, and protecting public health in nearby residential areas from the impact of emissions from industrial infrastructure.

Fig. 17.

Spatial average annual CO measurements for AQMSs during 2015–2020, with account of exceedances per annum that CO measurements violate WHO limitations

Daily PM_2.5 and PM₁₀

The spatiotemporal alternation of the monthly suspended particulate concentrations is presented in Fig. 18a, b for PM_2.5 and PM₁₀ respectively, through the years 2015–2020 for the investigated AQMSs 1 and 2. As PM_2.5 and PM₁₀ concentrations for the other foreseen AQMSs were subject to unreliability; they were not further assessed. Noticeably, the daily profiles for PM_2.5 and PM₁₀ have indicated a systematic pattern, where the maximum levels were recorded around the midday periods for the investigated AQMS, and relatively lower levels during early morning, evening, and night. Extensively, a PM_2.5 maximum of 80.6 µg/m³ was obtained for AQMS 1, and a PM₁₀ maximum of 165.3 µg/m³ for AQMS 2. The peaks in PM_2.5 and PM₁₀ daily concentrations coincided with dust events that commonly occur during midday in the Yanbu industrial complex as well as throughout the Arabian Peninsula. In addition, the pattern for PM_2.5 daily concentrations indicated the accumulation of other gaseous emissions during the midday period, when O₃ precursors reached significant levels, considering the industrial activities as well as the intense traffic during midday. As PM₁₀ daily concentrations could be extensively coupled with intense dust activity, a conclusion was highlighted due to the diverse spatiality in the AQMS’s location (15 km) as well as the assorted sizes of particulate matter. During midday periods, the ground temperature begins to rise, leading to the warm-up of the lower boundary of outdoor air. Hence, convective currents were responsible for mixing the lower warm layers to be substituted with the cold upper layers. These findings were revealed by Habeebullah (2013), Aina et al. (2014), Modaihsh et al. (2015), Munir et al. (2013); Munir et al. (2016), Dasari et al. (2020), Sharma et al. (2020), and Algarni et al. (2021). Maximum peaks in PM_2.5 and PM₁₀ concentrations frequently exceeded the recommended standards during the investigated daily intervals.

Fig. 18.

Average daily measurements of PM_2.5 and PM₁₀ concentrations during 2015–2020: a for AQMS 1 and b AQMS 2

Monthly PM_2.5 and PM₁₀

Figure 19a and b show the variability of average monthly PM_2.5 and PM₁₀ in situ concentrations for the investigated AQMSs 1 and 2 from 2015 to 2020. Measured data for other anticipated AQMSs were not evaluated due to unreliability. An analog trend to those of the daily PM_2.5 and PM₁₀ profiles was attained for the monthly PM_2.5 and PM₁₀ concentrations. The maximum levels were established through the hot months of the year, with relatively lower concentrations for monthly PM_2.5 and PM₁₀ through the cold months in the case study region. It was observed that a PM_2.5 maximum up to 210.0 µg/m³ was obtained for AQMS 1 in August 2015, and PM₁₀ maxima around 215.0 µg/m³ was reached for AQMS 2 in June 2019; hence, the monthly PM_2.5 and PM₁₀ levels were routinely violated. Especially during the summer months, thermal heating at the ground level and the increase in turbulence in the mixing layer enhanced buoyancy and the accumulation of anthropogenic air pollutants. Consequently, high numbers of exceedances during hot months were attained. Apart from natural phenomena like summer dusty winds, a local contribution to the monthly PM_2.5 and PM₁₀ levels was also foreseen to originate from the industrial activities and traffic-driven re-suspension of air pollutants, since the little rainfall favors the accumulation of particulates’ emissions in the hot months in the Yanbu industrial complex. Further improvements in emission inventories, especially due to industrial activities, and source apportionment data are strongly recommended.

Fig. 19.

Average monthly measurements of PM_2.5 and PM₁₀ concentrations during 2015–2020: a for AQMS 1 and b AQMS 2

Annual PM_2.5 and PM₁₀

A summary of PM_2.5 and PM₁₀ average annual concentrations is presented in Fig. 20 for AQMS 1 and 2, respectively. Due to their unreliability, PM_2.5 and PM₁₀ concentrations for other anticipated AQMSs were not further provided in the current assessment. The evaluation of the annual PM_2.5 and PM₁₀ concentration was based on continuous measurements collected from 2015 to 2020 and their comparison with thresholds. The statistical criteria mean and account of exceedances that annual PM_2.5 and PM₁₀ measurements violate RCY thresholds were assigned for the assessment of PM_2.5 and PM₁₀ annual patterns. Inconclusively, these PM_2.5 and PM₁₀ measurements showed unsystematic behavior for the considered AQMSs. For reviewing the potential impacts on public health, reports confirmed the relationship between several symptoms and the dust levels in the ambient air. PM_2.5 and PM₁₀ are directly responsible for respiratory diseases such as aggravation of asthma, irritation, increase in hospital admissions, and even daily mortality. Compared to RCY restrictions for annual PM_2.5 (15 μm/m³) and PM₁₀ (50 μm/m³), assigned AQMSs within the Yanbu industrial complex had recorded in situ concentrations exceeding the regulations’ thresholds, which might cause adverse side effects on public health. Accordingly, the exceedances when PM_2.5 and PM₁₀ outdoor levels violated the standards were higher than those for the other foreseen gaseous emissions; analyzed violations were found in the range of 51 to 100 and 101 to 151 for PM_2.5 and PM₁₀, respectively. Consequently, the AQI was deemed as moderately polluted for PM_2.5 and unhealthy for sensitive groups regarding PM₁₀ violations. On the other hand, the number of the exceedances was observed to be reduced with the subsequent years. Due to the uncontrolled thermal mixing of boundary layers, potential ways for reducing ambient air emissions could be suggested, such as decreasing the production rates, avoiding dense traffic by shifting to less meteorological activities during nights, or offering remote offices. Simply put, air pollutants accumulate to higher levels, which could be regarded as genuine environmental barriers that degrade the air quality and endanger public health.

Fig. 20.

Spatial average annual PM_2.5 and PM₁₀ measurements for AQMSs during 2015–2020, with account of exceedances per annum that PM_2.5 and PM₁₀ measurements violate RCY limitations

With the association of experimental data for emissions’ concentrations in the ambient air, strategic policies and investments supporting cleaner transport, energy-efficient premises, alternatives for energy supply-chain to lessen the operability of power generation facilities, better industrial and municipal waste management, etc. would establish regional and multisectoral cooperation for the mitigation of air pollution and other health impacts of the emissions from the Yanbu industrial complex on the environment, as well as serving as tools for assessing the health benefits of such mitigation measures in other analog regimes worldwide. Moreover, extensive monitoring of the outdoor air quality for regions containing massive industrial infrastructure and populated residential suburbs is a prerequisite, when local ambient air quality is to meet the standards. Follow-up and a regular assessment of the in situ air pollutants, which originate from diverse industrial complexes associated with nearby residential areas, through a long term is recommended to refine the merits of the abatement policies adopted by the regional authorities to protect public health.

Conclusion

A detailed assessment was conducted for concentrations of outdoor air pollutants at spatially diverse AQMSs within the Yanbu industrial complex and its suburbs from 2015 to 2020, examining major gaseous emissions classified as extremely dangerous at elevated outdoor levels, namely, SO₂, H₂S, NO₂, O₃, CO, PM_2.5, and PM₁₀ pollutants. It was found that the massive industrial applications and specific meteorological covariables had influenced the variation of the spatiotemporal gaseous concentration. Particularly, high SO₂, H₂S, and CO accumulations in ambient surrounding were obtained during midday periods and the cold months. While maxima in NO₂ concentrations were attained during the morning and evening periods of the day as well as through the cold months. On the contrary, peak figures in O₃ concentrations were observed during elevated temperature periods of the day and the hot months, whereas maxima PM_2.5 and PM₁₀ concentrations were accumulated during midday periods and the hot months of the year. Although in situ measurements of the air pollutants routinely exceeded the relevant national and international regulatory standards, the analysis of air quality in the case study area revealed that the exceedances typically decreased between 2015 and 2020, indicating that there is an improvement in air quality with the subsequent years. Taking into consideration the number of exceedances of the annual concentrations, the AQI was evaluated as good for the investigated gaseous emissions, moderately polluted for PM_2.5, and unhealthy for sensitive groups in the case of PM₁₀ accumulations in the outdoor environment. In this context, special policies should be implemented to reduce the hazards posed by gaseous emissions to the public’s health during periods associated with high anthropogenic activity, temperature inversions, and rising production rates in the relevant industrial facilities.

Author contribution

K. Al-Zboon substantially contributed to basic idea for the current study, acquisition, and interpretation of data. On the other hand, the original data were processed and analyzed by L. Al Samrraie, and S. Al Rabadi drafted the manuscript. K. Alrawashdeh critically contributed to reading and revising the manuscript for important intellectual content.

Data availability

The authors ensure that all data and materials are available to support their published claims.

Declarations

Ethical approval

Not applicable.

Consent to participate

Consent has been obtained from the responsible authorities at the institute/organization, where the work has been conducted before its submission for publication.

Consent for publication

All the authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors and are aware that with minor exceptions, no changes can be made to authorship once the paper is submitted.

Competing interests

The authors declare no competing interests.