Skip to main content
NCBI home page
Springer Nature - PMC COVID-19 Collection logoLink to Springer Nature - PMC COVID-19 Collection
. 2023 May 31;195(6):772. doi: 10.1007/s10661-023-11375-7

Significant variation in air quality in South Indian cities during COVID-19 lockdown and unlock phases

Shibani Navasakthi 1, Anuvesh Pandey 1, Jashanpreet Singh Bhari 1, Ashita Sharma 1,✉
PMCID: PMC10229396  PMID: 37253943

Abstract

With the spread of COVID-19 pandemic worldwide, the Government of India had imposed lockdown in the month of March 2020 to curb the spread of the virus furthermore. This shutdown led to closure of various institutions, organizations, and industries, and restriction on public movement was also inflicted which paved way to better air quality due to reduction in various industrial and vehicular emissions. To brace this, the present study was carried out to statistically analyze the changes in air quality from pre-lockdown period to unlock 6.0 in South Indian cities, namely, Bangalore, Chennai, Coimbatore, and Hyderabad, by assessing the variation in concentration of PM2.5, PM10, NO2, and SO2 during pre-lockdown, lockdown, and unlock phases. Pollutant concentration data was obtained for the selected timeframe (01 March 2020–30 November 2020) from CPCB, and line graph was plotted which had shown visible variation in the concentration of pollutants in cities taken into consideration. Analysis of variance (ANOVA) was applied to determine the mean differences in the concentration of pollutants during eleven timeframes, and the results indicated a significant difference (F (10,264) = 3.389, p < 0.001). A significant decrease in the levels of PM2.5, PM10, NO2, and SO2 during the lockdown phases was asserted by Tukey HSD results in Bangalore, Coimbatore, and Hyderabad stations, whereas PM10 and NO2 significantly increased during lockdown period in Chennai station. In order to understand the cause of variation in the concentration of pollutants and to find the association of pollutants with meteorological parameters, the Pearson correlation coefficient was used to study the relationship between PM2.5, PM10, NO2, and SO2 concentrations, temperature, rainfall, and wind speed for a span of 15 months, i.e., from January 2020 to March 2021. At a significant level of 99.9%, 99%, and 95%, a significant correlation among the pollutants, rainfall had a major impact on the pollutant concentration in Bangalore, Coimbatore, Hyderabad, and Chennai followed by wind speed and temperature. No significant influence of temperature on the concentration of pollutants was observed in Bangalore station.

Keywords: COVID-19, Lockdown, South Indian cities, Air quality, Pollutants, Trend analysis

Introduction

Exponential population growth along with industrialization and urbanization is the major factor for escalating air pollution (Kota et al., 2018). The advancements have helped in the economic growth of a country but have also paved way for environmental concerns worldwide. The increasing demands for any product and the uncontrolled and continuous modernization are ultimately deteriorating the quality of air that we breathe. Air pollution is of a major concern in developing nations.

Increasing air pollutant levels are result of uncontrolled emissions from industrial processes, automobile emissions, construction activities, burning of fossil fuels for the generation of electricity, agricultural activities, and also due to some natural phenomena like pollen dispersal, forest fires, volcanic eruptions, and unusual wind patterns (Ganguly et al., 2021; Singh et al., 2007). These sources release major air pollutants like NOx, SOx PM2.5, PM10, O3, CO, trace/heavy metals, and CO2 (Guttikunda et al., 2014). According to UNEP, residential fuel consumption (12.47 µg/m3) is the major contributor to PM2.5 in Asia, followed by industrial activity (7.56 µg/m3), anthropogenic fugitive combustion and industrial dust (6.05 µg/m3), energy production (6 µg/m3), agricultural activity (5.24 µg/m3), and transportation (4.02 µg/m3) (UNEP, 2022). Air pollution has serious implications on human health leading to acute and chronic diseases (Ayala et al., 2012; Bernstein et al., 2004). It can also prove fatal for vulnerable population (infants, aged, and patients) causing various degenerative diseases and cancer (Bernstein et al., 2004; Ganguly et al., 2021).

In India, with population explosion and enhanced anthropogenic activities like rapid urbanization, increased industrialization, escalated energy consumption, and surged vehicular and industrial emissions, rising air pollution level is one of the major concerns of the nation (Guttikunda et al., 2019; Sarkar et al., 2021). As the cities are experiencing a population explosion, there is an increased demand for the products which directly or indirectly lead to a substantial increase in pollutant levels (Ganguly et al., 2021; Guttikunda et al., 2014). Deteriorating air quality is leading to climate change and is also affecting the flora, fauna, and human beings.

According to a worldwide survey, out of the 30 most polluted cities in the world, 21 cities are Indian cities during the year 2019 (IQAir, 2020a; b; c). The average PM2.5 concentration in India was recorded as 58.08 µg/m3 which was 5 times higher than that of the World Health Organization recommendations (IQAir, 2020a; b; c). But this is comparatively less than the previous year concentration, i.e., 72.54 µg/m3 on 2018 (IQAir, 2020a; b; c). In 2020, the average PM2.5 concentration has dipped to 51.90 µg/m3 (IQAir, 2020a; b; c).

In December 2019, COVID-19 was identified and was declared as a pandemic (Wang & Su, 2020). To prevent its spread, many countries had imposed lockdown (Moorthy et al., 2020). The first case of COVID-19 in India was confirmed on January 2020 in Kerala and further triggered community spread (GOI, 2020). To contain the infection, India had strictly restricted the movement of people and had put a halt on transportation, thus reducing human interactions, enforcing curfew, prohibiting gatherings, and restricting the operation of industries, educational institutions, and various private and public sector units (GOI, 2020). Various studies have indicated that shutdown of industries, institutions, and organizations, restriction on human movement, and limited public transportation system have resulted in the improvement of ambient air quality (Mahato et al., 2020; Sarkar et al., 2021; Singh & Chauhan, 2020; Srivastava et al., 2020).

Ambient air quality depends on pollutant concentration which is dependent on various factors like vehicular and industrial emissions, local meterological conditions, chemical transformations in the air, and other natural processes. To monitor the concentration of pollutants, to study its variation during the pandemic, and to understand the factors responsible for fluctuation in its concentration, various scientists have used GIS and laboratory experiments and have also used the air quality data from various data sources (Ganguly et al., 2021; Guttikunda et al., 2019; Mahato et al., 2020; Sarkar et al., 2021; Singh et al., 2007; Srivastava et al., 2020). With the usage of softwares and statistical tools like ANOVA, correlation and parameters responsible for variation in air quality can be asserted.

The present study provides a statistical analysis of the variation in concentration of air pollutants in selected South Indian cities, namely, Bangalore, Chennai, Coimbatore, and Hyderabad, due to the imposed lockdown. Additionally, the impact of rainfall pattern and wind speed on the concentration of air pollutants was studied. Thus, the objectives of the study were to statistically analyze the short-term change in air quality in the selected South Indian cities and also understand the influence of rainfall pattern and wind speed on the concentration of air pollutants.

Methodology

Selection of study area

The South Indian cities were selected based on the population, transportation, industrial activity, and business setup. The stations were selected based on the escalating industrial activity and population. The analysis is done for 4 South Indian cities namely Bangalore, Chennai, Coimbatore, and Hyderabad. Bangalore is the capital of Karnataka and is the third most populous city in India. The city suffers significantly with respiratory issues. Human health is highly impacted due to the deteriorating air quality (Meda & Mathew, 2022). The station selected in Bangalore city is Peenya which is one of the biggest industrial areas in Asia (Meda & Mathew, 2022).

Chennai is the capital of state of Tamil Nadu. It is the sixth most populous city in India and is the 36th largest urban area in world by population. The city often has AQI values ranging from 59 to 147 which signifies that the city is moderately polluted (Laxmipriya et al., 2022). Manali station was selected in Chennai city which is an industrial and residential suburb of Chennai.

Coimbatore also known as Manchester of the South is the third largest city and one of the most industrialized cities of Tamil Nadu (Government of Tamil Nadu, 2021). The air quality is moderate in Coimbatore city throughout the year (CPCB 2019). The station established in SIDCO Kurichi Industrial Estate area was selected for collection of pollutant concentration in Coimbatore city.

The capital city of Telangana, Hyderabad, is one of the densely populated regions of the state and is the fourth most populous city in India (Government of Telangana, 2021). Rapid urbanization and human settlement have led to increase in industrial discharge and vehicular emissions leading to increasing air pollution. The station at ICRISAT headquarters located at Patencheru, Hyderabad, was chosen for the present study.

Data collection

To study the impact of COVID-19 lockdown on air quality, the data for four South Indian Cities, namely, Bangalore, Chennai, Coimbatore, and Hyderabad, were analyzed. The air quality data of each station for the period 01 March 2020 to 30 November 2020 was obtained from the Central Pollution Control Board, India (CPCB) (https://cpcb.nic.in/namp-data/). The daily concentration of the four key air pollutants PM2.5, PM10, NO2, and SO2 was collected for the present study. The air quality parameters were analyzed in eleven phases: pre-lockdown phase (from 01 to 23 March), 4 phases of lockdown (i.e., phase 1: From 24 March to 14 April; phase 2: from 15 April to 03 May; phase 3: from 4 to 17 May; and phase 4: from 18 to 31 May), and post-lockdown phase focusing on different unlock phases (i.e., unlock 1.0: from 1 to 30 June; unlock 2.0: from 1 to 31 July; unlock 3.0: from 1 to 31 August; unlock 4.0: from 1 to 30 September; unlock 5.0: from 1 to 31 October; and unlock 6.0: from 1 to 30 November).

Pollutant concentrations were also compared with weather conditions (temperature, rainfall, and wind speed) of respective cities. Monthly average temperature, rainfall, and wind speed of the selected stations were collected from the Power Project, NASA prediction of worldwide energy resources managed by the NASA (https://power.larc.nasa.gov/data-access-viewer/), i.e., from 01 January 2020 to 31 March 2021.

Data analysis

ANOVA (one way), Tukey HSD, and Pearson correlation coefficient (r) were computed using self-coded software on Microsoft Excel 2019 to statistically analyze the collected data.

Results and discussion

Variation in air pollutant concentrations

The concentration of the pollutants PM2.5, PM10, NO2, and SO2 in the selected cities is represented graphically in Fig. 1 to understand the changes in the concentration of pollutants for all the concerned stations for the various lockdown and unlock phases. Figure 1 represents the variation of PM2.5 concentration in Bangalore, Chennai, Coimbatore, and Hyderabad; Fig. 2 is the illustration of variation of PM10 concentration in the selected cities; Fig. 3 depicts the variation of NO2 concentration in the study area; Fig. 4 shows the variation of SO2 concentration in the selected stations of South Indian cities.

Fig. 1.

Fig. 1

Open in a new tab

24-h concentration of PM2.5 from 1 March 2020 to 30 November 2020 and ANOVA results at p ≤ 0.001 in Bengaluru (F = 3.776), Chennai (F = 16.174), Coimbatore (F = 17.063), and Hyderabad (F = 49.872)

Fig. 2.

Fig. 2

Open in a new tab

24-h concentration of PM10 from 1 March 2020 to 30 November 2020 and ANOVA results at p ≤ 0.001 in Bengaluru (F = 21.799), Chennai (F = 31.689), Coimbatore (F = 13.289), and Hyderabad (F = 50.199)

Fig. 3.

Fig. 3

Open in a new tab

24-h concentration of NO2 from 1 March 2020 to 30 November 2020 and ANOVA results at p ≤ 0.001 in Bengaluru (F = 10.832), Chennai (F = 14.511), Coimbatore (F = 65.257), and Hyderabad (F = 49.343)

Fig. 4.

Fig. 4

Open in a new tab

24-h concentration of SO2 from 1 March 2020 to 30 November 2020 and ANOVA results at p ≤ 0.001 in Bengaluru (F = 44.298), Chennai (F = 45.484), Coimbatore (F = 30.352), and Hyderabad (F = 20.588)

Line graphs for daily concentration of PM2.5, PM10, NO2, and SO2 were plotted for all the cities, and ANOVA and Tukey HSD were calculated to prove significant variation statistically. Single factor ANOVA was used to understand and find out the statistically significant variation between the various pollutant concentrations of selected South Indian cities during pre-lockdown, different lockdown, and unlock phases. It has been observed that the time period from pre-lockdown to unlock phase has a significant role in the variation of pollutant concentration. PM2.5, PM10, NO2, and SO2 showed significant differences (F (10,264) = 3.389, p < 0.001) in the concentration during selected different timeframes, i.e., pre-lockdown, different lockdown, and unlock period in all the four cities. The ANOVA results (F value) are depicted in Fig. 1.

In Peenya station, one of the industrial areas of Bangalore city, it was observed that PM2.5, PM10, and NO2 experienced a reduction in their concentration during the lockdown period and again started to increase with different unlock phases (Figs. 1, 2, and 3), whereas, in the case of SO2, the concentration started to dip by unlock phase 2 (Fig. 4). Similar observations were made by Jain and Sharma (2020), and this could be attributed to restrictions imposed during lockdown, and with different unlock phases, the restrictions imposed were waived off which could be the reason for progressive increase in the concentration during post-lockdown period. During lockdown and unlock phases, daily PM10 concentration ranged from 34.18–64.42 µg/m3 to 24.47–118.25µg/m3, respectively, and the minimum and maximum concentration during the selected timeframe was noted as 24.47 µg/m3 and 118.25 µg/m3. The daily PM2.5 concentration during lockdown and unlock phase ranged between 30.12–42.49 µg/m3 and 14.82–63.67µg/m3, respectively, and throughout the study period, the minimum and maximum concentration was observed as 14.28 µg/m3 and 63.67 µg/m3. NO2 concentration started increasing during the unlock phases. The minimum and maximum concentration of NO2 was 2.57 µg/m3 and 20.2 µg/m3. Daily SO2 concentration ranged from 2.07 to 4.64 µg/m3.

In Bangalore city, ANOVA results indicated that there has been a significant difference in the concentration of the pollutants during the selected timeframe. The pollutant concentration significantly changed during the lockdown period and started to increase with different unlock phases. Tukey HSD test revealed that there has been a significant decrease in PM2.5 concentration during pre-lockdown when compared to unlock phase 2 (Table 1). PM10 concentration is showing a significant decrease during lockdown period when compared with pre-lockdown phase, and significant increase is visible from Tukey test between lockdown phases and unlock phases. A similar trend is observed in case of NO2 concentration, whereas SO2 concentration is observed to have a significant decrease between the lockdown phase and unlock phases as the concentration is observed to decline with unlock phases except unlock phase 1.

Table 1.

Tukey HSD results for Bangalore station showing the difference between groups

LP 1 LP 2 LP 3 LP 4 UP 1 UP 2 UP 3 UP 4 UP 5 UP 6
PM2.5 PL 9.43 10.64 7.75 11.44 18.8 24.58* 14.91 20.27 13.22 13.4
LP 1 0 1.2 1.69 2 9.37 15.15 5.47 10.83 3.79 3.97
LP 2 0 2.89 0.8 8.16 13.95 4.27 9.63 2.59 2.77
LP 3 0 3.69 11.05 16.84 7.16 12.52 5.48 5.66
LP 4 0 7.36 13.15 3.47 8.83 1.78 1.96
UP 1 0 5.79 3.89 1.47 5.58 5.4
UP 2 0 9.68 4.32 11.36 11.18
UP 3 0 5.36 1.69 1.51
UP 4 0 7.04 6.86
UP 5 0 0.18
PM10 PL 37.1* 42.92* 31.58* 41.2* 47.53* 56.21* 37.76* 35.12* 19.18 8.96
LP 1 0 5.81 5.53 4.09 10.43 19.11 0.65 1.98 17.92 28.14#
LP 2 0 11.34 1.72 4.62 13.3 5.16 7.8 23.74# 33.95#
LP 3 0 9.62 15.96 24.64 6.18 3.54 12.39 22.61
LP 4 0 6.34 15.02 3.44 6.08 22.02 32.23#
UP 1 0 8.68 9.78 12.41 28.35# 38.57#
UP 2 0 18.46 21.09# 37.03# 47.25#
UP 3 0 2.64 18.58 28.79#
UP 4 0 15.94 26.16#
UP 5 0 10.22
NO2 PL 9.58* 9.89* 6.22 9.35* 9.52* 11* 8.11* 9.55* 4.67 3.95
LP 1 0 0.31 3.36 0.23 0.06 1.41 1.47 0.028 4.91 5.63
LP 2 0 3.67 0.55 0.37 1.1 1.78 0.34 5.22 5.95
LP 3 0 3.13 3.3 4.77 1.89 3.33 1.55 2.27
LP 4 0 0.17 1.65 1.24 0.21 4.68 5.4
UP 1 0 1.47 1.41 0.033 4.85 5.57
UP 2 0 2.88 1.44 6.33# 7.05#
UP 3 0 1.44 3.44 4.16
UP 4 0 4.88 5.61
UP 5 0 0.72
SO2 PL 0.15 0.12 0.067 0.17 0.84# 0.41 0.76* 0.72* 0.7* 0.48*
LP 1 0 0.029 0.081 0.024 0.69# 0.56* 0.9* 0.87* 0.85* 0.63*
LP 2 0 0.052 0.053 0.72# 0.53* 0.88* 0.84* 0.82* 0.6*
LP 3 0 0.1 0.77# 0.48 0.82* 0.79* 0.76* 0.55
LP 4 0 0.67# 0.58* 0.93* 0.89* 0.87* 0.65*
UP 1 0 1.25* 1.59* 1.56* 1.54* 1.32*
UP 2 0 0.35 0.31 0.29 0.071
UP 3 0 0.038 0.059 0.28
UP 4 0 0.021 0.24
UP 5 0 0.22
Open in a new tab

*Significant decrease in time phase in row with respect to time phase in the column

#Significant increase in time phase in row with respect to time phase in the column

PL, pre-lockdown phase

LP, lockdown phases

UP, unlock phases

With unlock phases, restrictions in industries and transportation were lifted partially leading to SO2 and NO2 emissions in the air from fossil fuel-based industries and combustion in automobiles which are the common anthropogenic sources of SOx and NOx (Bhanarkar et al., 2005; Chelani & Devotta, 2007; Kuttippurath et al., 2022). The unprecedented growth of vehicles in Bangalore since the last decade due to increasing passenger and freight travel demand along with the accelerating transportation demand due to increasing urban population dispersion is the reason behind exponential increase in automobile traffic which is responsible for approximately half of the city’s pollution (Harish, 2012; Nagendra et al., 2007).

Road dust, usage of diesel generators, and both construction activities are also the main drivers of air pollution in Bangalore (Chinnaswamy, 2016). The strict lockdown in Bangalore has helped in significant reduction in the air pollution levels across the city (Gouda et al., 2021). Substantial improvement in air quality was also observed by Rathore et al. (2021) in Bangalore city with 52% decline in AQI.

In Chennai city, Manali station was selected which is an industrial area in the Chennai Corporation and has numerous industries which cause air pollution by releasing harmful pollutants like PM2.5, PM10, NO2, O3, and SO2 into the environment (Hemamalini et al., 2022; Manju et al., 2002). Most of the industries established in this area are chemical industries, fertilizer industries, petroleum product industries, and cement manufacturing industries (Arulprakasajothi et al., 2020).

During the lockdown period, a slight decline was noted in the concentration of PM2.5 (6.34–19.22 µg/m3) and NO2 (3.23–14.42 µg/m3), whereas a slight increment was observed in the concentration of PM2.5 (11.52–50.42 µg/m3) during unlock period (Fig. 1). ANOVA results indicated that there was a significant difference in the concentration of pollutants at p ≤ 0.001. From Tukey HSD test, a significant increase in the concentration during unlock phase 5 and 6 with lockdown phases was observed for PM2.5 (Table 2). The minimum and maximum concentration of PM2.5 was 6.34 µg/m3 and 50.42 µg/m3. During the lockdown, the average PM2.5 levels in the peak hour decreased by 48.5% in Chennai when compared with the pre-lockdown period (Ravindra et al., 2021).

Table 2.

Tukey HSD results for Chennai station showing the difference between groups

LP 1 LP 2 LP 3 LP 4 UP 1 UP 2 UP 3 UP 4 UP 5 UP 6
PM2.5 PL 0.57 3.67 2.6 5.89 2.53 3.03 8.49 2.5 20.17# 21.76#
LP 1 0 4.23 3.17 5.32 1.96 2.46 7.92 1.94 19.6# 21.2#
LP 2 0 1.07 9.56 6.2 6.69 12.15 6.17 23.84# 25.43#
LP 3 0 8.49 5.13 5.63 11.09 5.1 22.77# 24.36#
LP 4 0 3.36 2.86 2.6 3.39 14.28 15.87#
UP 1 0 0.5 5.96 0.027 17.64# 19.23#
UP 2 0 5.46 0.52 17.14# 18.74#
UP 3 0 5.98 11.68 13.28#
UP 4 0 17.67# 19.26#
UP 5 0 1.59
PM10 PL 30.78 104.84# 77.08# 31.41 3.77 1.75 9.61 0.62 20.43 18.22
LP 1 0 74.06# 46.3# 0.63 27.01 29.03 21.17 30.15 10.35 12.56
LP 2 0 27.76 73.43* 101.07* 103.09* 95.23* 104.22* 84.42* 86.62*
LP 3 0 45.67* 73.31* 75.33* 67.47* 76.45* 56.65* 58.86*
LP 4 0 27.64 29.66 21.8 30.79 10.99 13.19
UP 1 0 2.02 5.84 3.15 16.65 14.45
UP 2 0 7.86 1.12 18.68 16.47
UP 3 0 8.98 10.82 8.61
UP 4 0 19.8 17.6
UP 5 0 2.2
NO2 PL 4.22 1.62 3.08 4.64 4.4 3.69 3.37 2.05 2.66 1.28
LP 1 0 2.6 1.14 8.86# 8.61# 7.91# 7.59# 2.17 1.56 2.94
LP 2 0 1.46 6.26 6.01# 5.3 4.99 0.44 1.05 0.34
LP 3 0 7.72# 7.47# 6.76# 6.45# 1.03 0.42 1.8
LP 4 0 0.25 0.96 1.27 6.69* 7.3* 5.92
UP 1 0 0.71 1.02 6.45* 7.06* 5.68*
UP 2 0 0.31 5.74* 6.35* 4.97*
UP 3 0 5.43* 6.04* 4.65
UP 4 0 0.61 0.77
UP 5 0 1.38
SO2 PL 0.6 0.52 0.4 0.22 0.27 2.39* 3.2* 3.18* 3.32* 1.44
LP 1 0 0.086 0.2 0.38 0.34 1.79* 2.6* 2.57* 2.72* 2.04#
LP 2 0 0.12 0.29 0.25 1.87* 2.68* 2.66* 2.8* 1.96#
LP 3 0 0.17 0.13 1.99* 2.8* 2.78* 2.92* 1.84#
LP 4 0 0.041 2.16* 2.97* 2.95* 3.1* 1.67
UP 1 0 2.12* 2.93* 2.91* 3.06* 1.71#
UP 2 0 0.81 0.79 0.93 3.83#
UP 3 0 0.023 0.12 4.64#
UP 4 0 0.14 4.62#
UP 5 0 4.76#
Open in a new tab

*Significant decrease in time phase in row with respect to time phase in the column

#Significant increase in time phase in row with respect to time phase in the column

PL, pre-lockdown phase

LP, lockdown phases

UP, unlock phases

The daily minimum and maximum concentration of PM10 throughout the selected time period was observed as 6.29 µg/m3 and 155.53 µg/m3, respectively. The impact of lockdown on air quality was unanticipated in the case of PM10 concentration, as an increasing trend was observed during the lockdown phases (6.29–155.33 µg/m3) and then decreased during unlock phases (30.77–71.77 µg/m3) (Fig. 2). PM10 and NO2 concentrations were significantly higher during the lockdown period when compared to pre-lockdown phase. PM10 concentration has shown a significant dip during unlock phases, and this could be due to wet deposition as this city received heavy rainfall during the month of June and July 2020 (SANDRP, 2020; Table 3).

Table 6.

Tukey HSD results for Hyderabad station showing the difference between groups

LP 1 LP 2 LP 3 LP 4 UP 1 UP 2 UP 3 UP 4 UP 5 UP 6
PM2.5 PL 0.56 6.69 6.44 2.24 17.05* 21.57* 22.58* 15.86* 12.33 23.12#
LP 1 0 6.14 5.88 1.68 16.49* 21.01* 22.02* 15.3* 12.88 23.68#
LP 2 0 0.25 4.45 10.36 14.88* 15.89* 9.17 19.02# 29.82#
LP 3 0 4.2 10.61 15.13 16.14* 9.42 18.77# 29.57#
LP 4 0 14.81 19.33* 20.34* 13.62 14.57 25.37#
UP 1 0 4.52 5.53 1.19 29.38# 40.17#
UP 2 0 1.01 5.71 33.9# 44.69#
UP 3 0 6.72 34.91# 45.71#
UP 4 0 28.19# 38.99#
UP 5 0 10.8
PM10 PL 16.44 18.9 18.95 14.33 47.19* 58.5* 58.59* 48.82* 8.66 35.27#
LP 1 0 2.46 2.51 30.77 30.75* 42.06* 42.14* 32.38* 25.11 51.71#
LP 2 0 0.051 33.22 28.29 39.6* 39.69* 29.93 27.56 54.16#
LP 3 0 33.27 28.24 39.55* 39.64* 29.87 27.61 54.21#
LP 4 0 61.51* 72.83* 72.91* 63.15* 5.66 20.94
UP 1 0 11.31 11.4 1.63 55.85# 82.45#
UP 2 0 0.085 9.68 67.17# 93.77#
UP 3 0 9.76 67.25# 93.85#
UP 4 0 57.49# 84.09#
UP 5 0 26.6#
NO2 PL 9.66* 6.77* 6.89* 5.18 10.04* 11.53* 12.24* 9.9* 2.72 5.88#
LP 1 0 2.89 2.76 4.48 0.39 1.87 2.58 0.25 6.93# 15.54#
LP 2 0 0.13 1.59 3.28 4.76 5.47* 3.14 4.04 12.64#
LP 3 0 1.72 3.15 4.64 5.35 3.01 4.17 12.77#
LP 4 0 4.87 6.35* 7.06* 4.73 2.45 11.05#
UP 1 0 1.49 2.2 0.14 7.32# 15.92#
UP 2 0 0.71 1.63 8.81# 17.41#
UP 3 0 2.34 9.52# 18.12#
UP 4 0 7.18# 15.78#
UP 5 0 8.6#
SO2 PL 2.22 2.08 2.29 0.72 2.1 2.57 4.02* 3.83 0.92 5.66#
LP 1 0 0.14 4.51 1.5 0.12 0.35 1.8 1.61 3.14 7.88#
LP 2 0 4.36 1.36 0.023 0.49 1.95 1.75 2.99 7.73#
LP 3 0 3.01 4.39 4.86* 6.31* 6.12* 1.37 3.37
LP 4 0 1.38 1.85 3.31 3.11 1.64 6.38#
UP 1 0 0.47 1.93 1.73 3.02 7.76#
UP 2 0 1.45 1.26 3.49 8.23#
UP 3 0 0.2 4.94# 9.68#
UP 4 0 4.75# 9.48#
UP 5 0 4.74#
Open in a new tab

*Significant decrease in time phase in row with respect to time phase in the column

#Significant increase in time phase in row with respect to time phase in the column

PL, pre-lockdown phase

LP, lockdown phases

UP, unlock phases

Whereas in the case of NO2, during unlock phases 1, 2, and 3, significant escalation was observed with respect to lockdown phases but started declining at unlock phase 4 (significantly at p ≤ 0.001) (Table 2). This decline during unlock phases 1, 2, and 3 may be due to temperature rise (Table 3) which could have led to the formation of tropospheric ozone by the photolytic reaction of NO2 in the presence of sunlight (Ambade, 2018). The daily minimum and maximum concentration of NO2 was observed as 3.23 µg/m3 and 20.14 µg/m3 (Fig. 3).

Daily SO2 concentration in Chennai ranged from 3.07 to 9.69 µg/m3 (Fig. 4). In the case of SO2 concentration, a significant dip in the concentration during unlock phases with respect to pre-lockdown and lockdown phases was observed which can be attributed to the trapping of pollutants near the ground due to the meteorological condition during that period (Table 3) and during unlock phase 6 significant rise can be noted. PM2.5, tropospheric NO2 concentration, and AQI had shown a pronounced decrease in Chennai during the lockdown period (Singh & Chauhan, 2020; Singh & Tyagi, 2021).

Significant variation in the concentration of the pollutants was observed in Chennai city. It is one of the fastest-growing cities with deteriorating air quality (Dutta et al., 2021). Rapid urbanization with increasing vehicle congestion and industrial growth is adding up to the gaseous pollutants and particulate matter in the ambient air of Chennai (Partheeban et al., 2020; Senthilnathan, 2008; Velmurugan & Reddy, 2005). From 2009 to 2018, drastic increase in the air pollutants level was observed by Anu et al. (2019) in most of the urban areas in Chennai city. PM2.5, PM10, and NO2 concentrations varied significantly during the lockdown period when compared with pre-lockdown period (Singh & Tyagi, 2021).

In Coimbatore, visible reduction in the concentration of PM2.5 (17.68–28.22 µg/m3) and PM10 (23.53–35.81 µg/m3) was noted during lockdown, whereas during unlock phases, the concentration of PM2.5 (12.14–49.91 µg/m3) and PM10 (16.53–56.26 µg/m3) escalated (Figs. 1 and 2). NO2 concentration varied erratically during the selected time period with minimum and maximum concentrations as 6.93 µg/m3 and 75.64 µg/m3, respectively (Fig. 3). Das et al. (2021) have observed the decline in the concentration of PM2.5, PM10, SO2, and O3 during the lockdown period and have also concluded that the NO2 concentration was highest in the Coimbatore.

At a significant level of 99.9%, ANOVA results obtained have shown that there was a significant difference in the pollutants concentration during the study period. From Table 4, it is noted that PM2.5, PM10, and SO2 concentrations had shown a significant increase in their concentrations during unlock 6.0 phase, and the possible reason could be due to upliftment of curfew during unlock phases leading to emissions from industries and vehicles (Manju et al., 2018). From Tukey test, it was also observed that NO2 concentration significantly decreased during unlock phases when compared to pre-lockdown and lockdown phases and which could be either due to the consumption of nitrogen oxides for ozone formation (Ambade, 2018) or due to wet deposition during that time period as given in Table 5.

Table 3.

Pollutant concentration, average rainfall, wind speed, and temperature data of Chennai

Months Concentration of pollutants (µg/m3) Meteorological parameters
PM2.5 PM10 NO2 SO2 Rainfall (mm/day) Wind speed (m/s) Temperature (°C)
Jan-20 40.72 ± 21.50 64.24 ± 28.99 7.03 ± 1.91 15.12 ± 4.97 0.87 2.52 24.75
Feb-20 24.45 ± 6.02 66.52 ± 19.43 6.23 ± 3.63 11.48 ± 3.62 0.01 2.91 25.98
Mar-20 10.99 ± 10.16 37.66 ± 36.22 7.9 ± 3.78 7.32 ± 0.46 0.02 2.88 28.8
Apr-20 11.27 ± 5.34 114.83 ± 43.38 6.83 ± 2.08 6.86 ± 0.14 1.56 3.15 30.55
May-20 14.32 ± 5.21 9.81 ± 45.55 7.09 ± 4.12 101.88 ± 0.15 2.94 3.12 31.19
Jun-20 15.47 ± 6.91 46.46 ± 28.30 13.84 ± 1.75 7.17 ± 0.46 2.43 3.01 30.73
Jul-20 15.62 ± 10.36 40.68 ± 21.37 13.14 ± 2.93 5.12 ± 1.46 6.28 2.88 28.47
Aug-20 21.14 ± 9.29 48.96 ± 15.48 12.80 ± 7.74 4.22 ± 0.54 3.86 2.72 28.37
Sep-20 15.21 ± 4.89 40.53 ± 12.06 7.26 ± 3.72 4.33 ± 0.31 5.91 2.76 27.63
Oct-20 31.97 ± 13.21 58.30 ± 13.93 6.65 ± 3.37 3.86 ± 1.84 4.85 2.31 27.43
Nov-20 35.63 ± 23.10 58.68 ± 23.88 8.26 ± 6.81 8.85 ± 2.59 15.36 3.42 26.28
Dec-20 38.50 ± 16.06 84.28 ± 22.78 7.49 ± 1.96 10.35 ± 1.14 6.9 3.45 24.58
Jan-21 41.80 ± 16.57 81.46 ± 27.31 10.74 ± 1.83 8.53 ± 0.41 3.74 2.77 24.63
Feb-21 48.29 ± 12.78 80.50 ± 19.81 17.03 ± 6.51 9.28 ± 2.21 0.94 2.65 24.24
Mar-21 34.40 ± 13.39 69.08 ± 27.54 14.71 ± 1.78 13.21 ± 16.38 0 2.22 27.55
Open in a new tab

Table 7.

Pollutant concentration, average rainfall, wind speed, and temperature data of Bengaluru

Months Concentration of pollutants (µg/m3) Meteorological parameters
PM2.5 PM10 NO2 SO2 Rainfall (mm/day) Wind speed (m/s) Temperature (°C)
Jan-20 44.47 ± 10.26 101.67 ± 22.03 35.92 ± 4.81 3.45 ± 0.30 0.01 2.4 22.59
Feb-20 45.71 ± 6.49 108.13 ± 37.51 27.30 ± 5.92 3.38 ± 0.20 0 2.91 24.51
Mar-20 44.06 ± 9.46 76.00 ± 24.68 14.10 ± 5.29 3.33 ± 0.28 0.27 2.67 27.47
Apr-20 36.40 ± 8.15 43.86 ± 11.07 6.20 ± 0.64 3.56 ± 0.51 2.05 2.66 27.97
May-20 35.87 ± 9.22 7.99 ± 11.87 3.45 ± 3.77 4.697 ± 0.36 1.85 2.57 28.44
Jun-20 27.64 ± 6.60 37.16 ± 10.91 6.51 ± 0.74 4.19 ± 0.32 5.74 3.8 24.54
Jul-20 21.36 ± 2.94 27.88 ± 5.75 5.03 ± 1.05 3.00 ± 0.74 6.08 2.95 22.97
Aug-20 30.04 ± 23.93 45.48 ± 12.62 7.94 ± 13.82 2.58 ± 0.24 3.18 3.55 23.14
Sep-20 26.86 ± 23.08 48.95 ± 13.49 6.37 ± 2.31 2.63 ± 0.22 6.42 3.03 22.28
Oct-20 31.80 ± 20.09 62.39 ± 27.75 11.20 ± 1.21 2.65 ± 0.27 5.07 2 22.05
Nov-20 33.54 ± 29.76 77.46 ± 33.57 11.93 ± 2.71 2.87 ± 0.32 3.31 2.43 21.08
Dec-20 37.51 ± 16.52 111.33 ± 30.49 27.64 ± 9.16 2.98 ± 0.61 0.93 2.47 19.74
Jan-21 43.84 ± 11.05 148.79 ± 74.14 22.09 ± 2.36 4.76 ± 1.34 0.51 2.43 20.88
Feb-21 43.52 ± 10.05 86.90 ± 13.34 26.13 ± 3.06 5.97 ± 1.11 0.57 2.52 21.65
Mar-21 47.04 ± 16.31 105.03 ± 29.80 33.03 ± 5.28 5.69 ± 0.68 0 2.77 25.99
Open in a new tab

Urbanization, industrialization, and escalating population growth leading to an increase in frequent vehicular traffic and traffic congestion in Coimbatore city have impacted the air quality of the city (Vijayanand et al., 2008). The major contributor to air pollution in Coimbatore is increasing vehicular emissions and emissions from the foundry and textile industries (Manju et al., 2018; Mohanraj & Azeez, 2005). A study conducted by Arunkumar et al. (2022) on air quality in Coimbatore city has found that the particulate matter concentration exceeds the National Ambient Air Quality Standards. SIDCO Kurichi, Coimbatore, is an industrial area with both small- and large-scale industries, and emissions from these industries are a prominent factor for air pollution (Aravind, 2016). Pollutant concentration is majorly due to increasing vehicular emissions, and it is also influenced by meteorological factors (Geetha & Kokila, 2015; Madhavan & Meenambal, 2010).

As in the case of Hyderabad, there has been a noticeable reduction in the concentration of PM2.5, NO2, and PM10 during the lockdown phase (Figs. 1, 2, and 3). During lockdown and unlock phases, daily PM10 concentration ranged between 49.33–123.59 µg/m3 and 16.62–158.52 µg/m3, respectively, and the minimum and maximum concentration during the selected timeframe was 16.62 µg/m3 and 158.52 µg/m3 (Fig. 2). The daily PM2.5 concentration during the lockdown and unlock phases ranged from 23.15–37.38 µg/m3 to 7.03–75.45 µg/m3, respectively, and throughout the study period, the minimum and maximum concentration was observed as 7.03 µg/m3 and 75.45 µg/m3 (Fig. 1). The minimum and maximum daily concentration of NO2 during the study period was observed as 4.78 µg/m3 and 26.48 µg/m3 (Fig. 3). Daily SO2 concentration ranged from 2.63 to 21.89 µg/m3 (Fig. 4).

ANOVA results indicated a statistically significant variation in the concentration of pollutants in Hyderabad city during the study period. Tukey HSD test revealed a significant increase in the concentration of PM2.5, PM10, NO2, and SO2 during different unlock phases as significant difference was observed between lockdown and unlock phases (Table 6). During the lockdown period, no significant change in the concentration of NO2 was observed. This may be attributed to the high residence time of NO2 and weather conditions which allows it to stay in minimum concentration in the atmosphere (Prather et al., 2015). But the concentration significantly increased during unlock 5.0.

Table 4.

Tukey HSD results for Coimbatore station showing the difference between groups

LP 1 LP 2 LP 3 LP 4 UP 1 UP 2 UP 3 UP 4 UP 5 UP 6
PM2.5 PL 8.24 12.65* 10.44 13.19* 19.49* 10.8* 17.89* 16.25* 5.76 0.65
LP 1 0 4.41 2.2 4.95 11.25* 2.56 9.65 8.01 2.48 8.89
LP 2 0 2.21 0.54 6.84 1.85 5.24 3.61 6.88 13.29#
LP 3 0 2.75 9.05 0.36 7.45 5.81 4.68 11.09
LP 4 0 6.3 2.39 4.7 3.07 7.42 13.83#
UP 1 0 8.69 1.6 3.24 13.73* 20.14#
UP 2 0 7.09 5.45 5.04 11.45#
UP 3 0 1.64 12.13# 18.54#
UP 4 0 10.49# 16.9#
UP 5 0 6.41
PM10 PL 18.71* 23.45* 20.71* 18.09* 27.54* 15.29* 24.57* 20.76* 10.01 8.89
LP 1 0 4.75 2.01 0.62 8.83 3.42 5.86 2.05 8.7 9.82
LP 2 0 2.74 5.37 4.09 8.16 1.12 2.69 13.44 14.56#
LP 3 0 2.63 6.83 5.42 3.86 0.046 10.7 11.82
LP 4 0 9.46 2.79 6.49 2.68 8.07 9.19
UP 1 0 12.25 2.97 6.78 17.53# 18.65#
UP 2 0 9.28 5.47 5.28 6.4
UP 3 0 3.81 14.56# 15.68#
UP 4 0 10.75 11.87
UP 5 0 1.12
NO2 PL 1.47 1.84 2.16 2.1 2.28* 4.24* 1.9* 1.72 1.77 1.93*
LP 1 0 0.37 0.7 0.63 0.82 2.78* 0.43 0.26 0.3 3.39*
LP 2 0 0.33 0.26 0.45 2.41* 0.06 0.11 0.07 3.76*
LP 3 0 0.066 0.12 2.08 0.27 0.44 0.4 4.09*
LP 4 0 0.18 2.14* 0.2 0.38 0.33 4.02*
UP 1 0 1.96* 0.39 0.56 0.52 4.21*
UP 2 0 2.35# 2.52* 2.48* 6.17*
UP 3 0 0.17 0.13 3.82*
UP 4 0 0.043 3.65#
UP 5 0 3.69#
SO2 PL 19.63* 12.65 18.72* 1.96 2.19 25.26* 13.22* 28.32* 24.51* 22.06#
LP 1 0 6.99 38.35* 21.6* 21.82* 44.9* 32.85* 47.96* 44.14* 41.69#
LP 2 0 31.37* 14.61 14.84* 37.91* 25.86* 40.97* 37.16* 34.7#
LP 3 0 16.76* 16.53* 6.54 5.5 9.6 5.79 3.34
LP 4 0 0.23 23.3* 11.25 26.36# 22.55# 20.09#
UP 1 0 23.07* 11.03# 26.13# 22.32# 19.87#
UP 2 0 12.05# 3.06 0.75 3.21
UP 3 0 15.11* 11.29* 8.84
UP 4 0 3.81 6.27
UP 5 0 2.45
Open in a new tab

*Significant decrease in time phase in row with respect to time phase in the column

#Significant increase in time phase in row with respect to time phase in the column

PL, pre-lockdown phase

LP, lockdown phases

UP, unlock phases

Allu et al. (2021) in his study at Hyderabad city observed an increase in O3 concentration from pre-lockdown and lockdown period due to decrease in CO and NOx concentration and had also detected reduction in NO2, NO, and CO concentration by 33.7%, 53.8%, and 27.25%, respectively. Increasing air pollution in Hyderabad is largely influenced by growing number of vehicle, industries, and burning wastes (Dholakia et al., 2014; Guttikunda & Kopakka, 2014). The prominent factors contributing to air pollution in Hyderabad include suspended dust, vehicular pollution, combustion, and industrial and refuse burning (Gummeneni et al., 2011). ANOVA and Tukey results for Hyderabad indicate that there has been a significant variation in the concentration of the pollutant due to the COVID-19 lockdown as significant decrease during the lockdown phase and an increase during unlock phases 5 and 6 were noted due to sudden halt in industrial activities, transportation sector, and strict curb on other human activities during lockdown and lifting of curfew during unlock phases leading to increase in the emissions.

Among the selected cities, the highest concentration of PM2.5 was observed in Hyderabad (75.45 µg/m3) while that of NO2 was in Coimbatore (75.64 µg/m3), PM10 was highest in Hyderabad (158.52 µg/m3), and that of SO2 in Hyderabad (21.89 µg/m3), respectively. Hyderabad city had minimum concentration of PM2.5 (7.03 µg/m3) and NO2 (2.5703 µg/m3) among the selected cities. The minimum concentration of PM10 was observed in Chennai city (6.29 µg/m3) and that of SO2 was observed in Bangalore city (2.07 µg/m3).

Similar trends were observed by Sahoo et al. (2021), wherein PM2.5, PM10, NOx, and CO were substantially reduced during the lockdown phases in Maharashtra with the maximum reduction in cities with high traffic volume which resulted in “satisfactory” AQI. Kumar et al. (2020) had observed reductions in PM2.5 concentrations in Chennai, Delhi, Hyderabad, Kolkata, and Mumbai. Further aerosol loading was also decreased by 29% in Chennai, 11% in Delhi, 4% in Kolkata, and 1% in Mumbai against the 2019 data. AQI determined using Pearson’s correlation analysis and unpaired Welch’s two-sample t test had shown significant decline in AQI at ITO-Delhi, Worli-Mumbai, Jadavpur-Kolkata, and Manali Village-Chennai by 44%, 59%, 59%, and 6%, respectively (Pant & Alka, 2020). Remarkable decrease in the concentration of PM2.5, PM10, SO2, and CO during the lockdown was seen in four major IT hubs of India, namely, Bengaluru, Chennai, Hyderabad, and Pune (Eregowda et al., 2021). Significant reduction in air quality of Indian region was observed by Gautam (2020) using the results from NASA.

Correlation between the air pollutants, average temperature, rainfall, and wind speed

To analyze and understand the association among the pollutants and its association with rainfall and wind speed, Pearson correlation coefficient was calculated between PM2.5, PM10, NO2, and SO2 concentrations and monthly average temperature, rainfall, and wind speed from January 2020 to March 2021 for the selected cities, and the data is represented in Table 7 (Bangalore), Table 3 (Chennai), Table 5 (Coimbatore), and Table 8 (Hyderabad).

Table 5.

Pollutant concentration, average rainfall, wind speed, and temperature data of Coimbatore

Months Concentration of pollutants (µg/m3) Meteorological parameters
PM2.5 PM10 NO2 SO2 Rainfall (mm/day) Wind speed (m/s) Temperature (°C)
Jan-20 37.38 ± 9.45 50.73 ± 14.45 40.44 ± 21.07 7.93 ± 2.33 0.14 1.66 23.17
Feb-20 39.75 ± 6.59 52.43 ± 10.34 34.92 ± 15.95 10.71 ± 2.33 0.11 1.97 25.21
Mar-20 32.88 ± 8.73 44.38 ± 16.10 47.31 ± 11.19 8.04 ± 1.06 0.5 1.45 28.39
Apr-20 23.38 ± 7.06 28.18 ± 13.84 64.11 ± 12.41 6.51 ± 0.71 1.23 1.39 29.21
May-20 22.73 ± 6.39 6.16 ± 9.27 29.25 ± 15.84 33.95 ± 0.41 2.19 1.98 27.81
Jun-20 14.57 ± 3.06 21.19 ± 5.09 42.52 ± 5.37 5.96 ± 0.42 3.64 3.1 25.1
Jul-20 23.15 ± 10.02 33.19 ± 12.61 19.49 ± 15.14 3.97 ± 2.01 7.52 2.45 23.9
Aug-20 16.54 ± 6.17 24.06 ± 8.57 30.85 ± 5.38 6.37 ± 0.36 6.35 2.87 23.87
Sep-20 17.43 ± 5.26 27.96 ± 4.74 16.74 ± 7.75 6.53 ± 0.37 6.45 2.55 23.45
Oct-20 27.68 ± 9.03 38.07 ± 13.15 19.57 ± 4.51 6.48 ± 0.38 5.44 1.83 23.69
Nov-20 35.71 ± 14.96 40.76 ± 14.77 22.49 ± 5.36 9.95 ± 3.69 5.3 1.59 23.19
Dec-20 45.84 ± 17.57 51.41 ± 18.67 14.70 ± 5.18 15.62 ± 1.51 2.3 1.92 21.69
Jan-21 40.49 ± 17.23 51.07 ± 15.73 17.12 ± 3.41 16.38 ± 1.84 3.57 1.86 21.94
Feb-21 46.58 ± 10.27 49.15 ± 10.82 14.72 ± 1.93 17.47 ± .96 0.75 1.68 23.11
Mar-21 49.06 ± 2.73 67.71 ± 25.93 25.55 ± 9.06 19.11 ± 2.16 0.85 1.67 26.14
Open in a new tab

Table 8.

Pollutant concentration, average rainfall, wind speed, and temperature data of Hyderabad

Months Concentration of pollutants (µg/m3) Meteorological parameters
PM2.5 PM10 NO2 SO2 Rainfall (mm/day) Wind speed (m/s) Temperature (°C)
Jan-20 47.87 ± 15.45 99.18 ± 36.75 21.37 ± 9.06 9.04 ± 5.94 0.38 2.43 22.57
Feb-20 41.03 ± 13.72 97.27 ± 31.64 22.10 ± 10.45 9.12 ± 4.77 0.05 2.69 24.89
Mar-20 32.63 ± 7.45 77.36 ± 17.27 13.10 ± 6.79 6.74 ± 2.01 0.53 2.05 29.02
Apr-20 28.83 ± 7.63 63.52 ± 17.47 7.73 ± 4.03 4.47 ± 1.32 0.58 2.44 32.07
May-20 19.47 ± 6.29 48.57 ± 28.44 6.35 ± 4.09 4.33 ± 3.18 1.8 2.45 33.28
Jun-20 15.82 ± 6.52 33.94 ± 14.02 5.56 ± 2.26 4.91 ± 1.24 5.44 3.52 27.43
Jul-20 10.78 ± 2.79 22.27 ± 7.18 4.03 ± 1.81 4.51 ± 0.73 8.24 3.05 25.26
Aug-20 9.85 ± 4.99 22.09 ± 9.28 3.36 ± 1.87 3.00 ± 1.04 6.63 3.74 24.84
Sep-20 15.95 ± 5.75 30.59 ± 12.37 5.58 ± 2.48 3.20 ± 1.18 8.03 2.12 24.73
Oct-20 43.16 ± 20.44 86.39 ± 40.03 12.26 ± 5.68 7.19 ± 3.97 5.88 1.75 23.74
Nov-20 56.79 ± 18.30 118.06 ± 38.9 21.65 ± 6.78 13.33 ± 7.14 0.23 2.2 20.91
Dec-20 65.24 ± 11.94 134.33 ± 24.7 33.61 ± 17.64 11.67 ± 5.33 0 1.7 18.77
Jan-21 62.25 ± 11.93 122.30 ± 25.5 17.47 ± 5.01 9.11 ± 2.10 0 1.89 21.65
Feb-21 61.04 ± 9.18 129.49 ± 20.9 23.15 ± 7.59 10.25 ± 5.79 0 2.02 22.72
Mar-21 52.26 ± 14.53 127.01 ± 23.7 18.38 ± 6.25 9.53 ± 3.87 0 2.3 28.55
Open in a new tab

The Pearson correlation coefficient between pollutant concentrations, average temperature, rainfall, and wind speed for the selected cities is tabulated in Table 9. It was observed from the table that in Bangalore station, NO2 is positively correlated with PM2.5 and PM10 at a significant level of 99.9% and PM2.5 and PM10 are significantly correlated at 99.9%. In Coimbatore station, PM2.5 is showing a statistically significant positive correlation with PM10 at a significant level of 99.9%. Furthermore, PM2.5, PM10, NO2, and SO2 are positively correlated at 99.9% at Hyderabad station, thus indicating a common source of emission which may be due to increased vehicular or industrial emissions during pre-lockdown and Unlock phases (Ali & Athar, 2010).

Table 9.

Pearson correlation coefficient (r) between the pollutants and meteorological parameters during the selected timeframe in the chosen cities

City Parameters PM2.5 PM10 NO2 SO2 Rainfall Wind speed Temperature
Bangalore PM2.5 1
PM10 0.726** 1
NO2 0.793*** 0.833*** 1
SO2 0.027  − 0.431  − 0.271 1
Rainfall  − 0.948***  − 0.658**  − 0.750**  − 0.109 1
Wind speed  − 0.419  − 0.395  − 0.354  − 0.108 0.378 1
Temperature 0.149  − 0.474  − 0.305 0.493  − 0.163 0.199 1
Chennai PM2.5 1
PM10 0.435 1
NO2 0.251 0.033 1
SO2  − 0.195  − 0.516*  − 0.209 1
Rainfall 0.125  − 0.100  − 0.162  − 0.092 1
Wind speed -0.233 0.039  − 0.316 0.198 0.519* 1
Temperature  − 0.876***  − 0.420  − 0.061 0.383  − 0.130 0.142 1
Coimbatore PM2.5 1
PM10 0.880*** 1
NO2  − 0.315  − 0.229 1
SO2 0.358  − 0.052  − 0.258 1
Rainfall  − 0.591*  − 0.443  − 0.437  − 0.378 1
Wind speed  − 0.661**  − 0.506  − 0.169  − 0.265 0.603* 1
Temperature  − 0.276  − 0.327 0.768*** 0.147  − 0.390  − 0.255 1
Hyderabad PM2.5 1
PM10 0.987*** 1
NO2 0.910*** 0.914*** 1
SO2 0.921*** 0.923*** 0.918*** 1
Rainfall  − 0.765***  − 0.818***  − 0.739**  − 0.718** 1
Wind speed  − 0.719**  − 0.694**  − 0.598*  − 0.554* 0.480 1
Temperature  − 0.575*  − 0.481  − 0.625*  − 0.618* 0.056 0.292 1
Open in a new tab

*Significant at p ≤ 0.05

**Significant at p ≤ 0.01

***Significant at p ≤ 0.001

In Bangalore, it was observed that with an increase in monthly average rainfall, PM2.5, PM10, and NO2 concentration decreases. No significant correlation was established between temperature and pollutants concentration. In the case of Chennai city, it was observed that PM2.5 concentration is decreasing with increasing temperature. No significant correlation between rainfall and pollutants was observed indicating continuous rainfall throughout the selected time frame in Chennai station. For Coimbatore station, PM2.5 has an inverse relation with monthly average rainfall and wind speed, whereas no influence on PM10 was noted which may be attributed to less intensity of wind preventing the dispersion of suspended particulate matter. NO2 and the monthly average temperature are positively correlated at a level of 99%. A significant correlation was noted for Hyderabad city wherein with an increase in monthly average rainfall and wind speed, there is a decrease in the concentration of PM2.5, PM10, NO2, and SO2. It was also noted that the monthly average temperature influences the concentration of PM2.5, NO2, and SO2 inversely at a significant level of 95%.

Most of the air pollutants concentration is largely influenced by meteorological factors like atmospheric pressure, relative humidity, wind speed, rainfall, and temperature (Tian et al., 2014). Jacob and Winner (2009) reported that generally an increase in temperature concentration of particulate matter decreases, whereas ozone concentration increases. An inverse relation was found between NO2 and temperature suggesting the formation of surface ozone by consumption of nitrogen oxides in the presence of sunlight and atmospheric heat leading to a decrease in the concentration of NO2 with an increase in temperature (Schroeder et al., 2017). Temperature difference also triggers ground air pollution by the formation of a temperature inversion layer, thus hampering the dispersion of pollutants by trapping them near the ground (Liu et al., 2020).

Rainfall can remove atmospheric pollutants, especially particulate matter directly by wet deposition or scavenging by precipitation and also constricts the movement of surface dust which in turn reduces the concentration of pollutants in the ambient air (Deng, 2012; Gao et al., 2019; Xu et al., 2017). Similarly, acid rain results in the removal of SO2 and NOx from the atmosphere by wet deposition (Possanzini et al., 1988). Wind speed is known to disperse the pollutants in the atmosphere, thus diluting their concentration in the ambient air which means the concentration of the pollutants decreases in a particular area (Galindo et al., 2011; Li et al., 2015). The concentration of air pollutants decreases with an increase in temperature, rainfall, and wind speed because of wet deposition by precipitation and dispersion by wind. Hence, meteorological factors highly influence the air pollutant concentration in the ambient air.

Conclusion

The present study has yielded a significant outcome that the air quality improved from pre-lockdown period to lockdown period and had again started deteriorating with Unlock phases. ANOVA and Tukey HSD results indicated that there was a statistically significant variation and difference in the concentration of the pollutants in all the selected South Indian cities during pre-lockdown, lockdown, and unlock phases (F (10,264) = 3.389, p < 0.001). Bangalore city had shown a significant reduction in the concentration of PM2.5, PM10, NO2, and SO2 during lockdown period. In Coimbatore, the concentration of PM2.5 and PM10 reduced during the shutdown phase, whereas NO2 increased during lockdown. Data from the Hyderabad station revealed that there was a reduction in the pollutant concentration during the lockdown period. In contrast to the common perception of a decline in pollutants during the lockdown phase, it was observed that the PM10 and NO2 concentration increased during the lockdown in Chennai due to weather conditions. Pearson correlation coefficient results indicated that meteorological factors have a major influence on the pollutant concentration in the atmosphere. Significant influence of rainfall on PM2.5 (Bangalore, Coimbatore, Hyderabad), PM10, NO2 (Bangalore and Hyderabad), and SO2 (Hyderabad) was observed. In the case of wind speed, a significant correlation with PM2.5, PM10, NO2, and SO2 was obtained in Hyderabad station, whereas temperature had no influence on the concentration of pollutants in Bangalore station. Hence, the study revealed that the lockdown had proved beneficial for the environment as the air quality worldwide had significantly improved during that period.

Author contribution

All authors contributed to the study conception and design. The data was collected and analyzed by Shibani Navashakthi, Anuvesh Pandey, and Jashanpreet Singh Bhari and helped in analysis of data and preparation of manuscript draft. Ashita Sharma is the mentor for the project and has guided the students for analysis and worked on finalizing the draft.

Data availability

Not applicable.

Declarations

Ethical approval

The research does not involve any human participants and/or animals.

Consent to participate

Not applicable.

Consent to publish

Not applicable.

Competing Interests

The authors declare no competing interests.

Footnotes

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. Ali M, Athar M. Impact of transport and industrial emissions on the ambient air quality of Lahore City, Pakistan. Environmental Monitoring Assessment. 2010;171:353–363. doi: 10.1007/s10661-009-1283-0. [DOI] [PubMed] [Google Scholar]
  2. Allu SK, Reddy A, Srinivasan S, et al. Surface ozone and its precursor gases concentrations during COVID-19 lockdown and pre-lockdown periods in Hyderabad City, India. Environmental Processes. 2021;8:959–972. doi: 10.1007/s40710-020-00490-z. [DOI] [Google Scholar]
  3. Ambade B. The air pollution during Diwali festival by the burning of fireworks in Jamshedpur city, India. Urban Climate. 2018;26:149–160. doi: 10.1016/j.uclim.2018.08.009. [DOI] [Google Scholar]
  4. Anu VM, Gladence LM, Dhanalakshmi R, et al. Recommendation and prediction system to control air pollution in selected regions of Chennai. International Journal of Recent Technology and Engineering. 2019;8:2336–2341. doi: 10.35940/ijrte.B1264.0982S1119. [DOI] [Google Scholar]
  5. Aravind TP. Monitoring, analysis and modelling of ambient air quality status at Indoshell Mould Ltd, Sidco, Coimbatore using artificial neural network. International Journal of Scientific Research in Science, Engineering and Technology. 2016;2:1172–1117. [Google Scholar]
  6. Arulprakasajothi M, Chandrasekhar U, Yuvarajan D, Teja MD. An analysis of the implications of air pollutants in Chennai. International Journal of Ambient Energy. 2020;41:209–213. doi: 10.1080/01430750.2018.1443504. [DOI] [Google Scholar]
  7. Arunkumar M, Manisekar A, Dhanakumar S. Influence of urbanization on particulate matter pollution in Coimbatore City, India. Ecology, Environment and Conservation. 2022;28:790–801. doi: 10.53550/EEC.2022.v28i02.033. [DOI] [Google Scholar]
  8. Ayala A, Brauer M, Mauderly JL, et al. Air pollutants and sources associated with health effects. Air Quality, Atmosphere and Health. 2012;5:151–167. doi: 10.1007/s11869-011-0155-2. [DOI] [Google Scholar]
  9. Bernstein JA, Alexis N, Barnes C, Bernstein IL, et al. Health effects of air pollution. Journal of Allergy and Clinical Immunology. 2004;114:1116–1123. doi: 10.1016/j.jaci.2004.08.030. [DOI] [PubMed] [Google Scholar]
  10. Bhanarkar AD, Goyal SK, Sivacoumar R, Rao CC. Assessment of contribution of SO2 and NO2 from different sources in Jamshedpur region, India. Atmospheric Environment. 2005;39:7745–7760. doi: 10.1016/j.atmosenv.2005.07.070. [DOI] [Google Scholar]
  11. Central Pollution Control Board India. (2019). National ambient air quality monitoring programme data 2019. https://cpcb.nic.in/namp-data/ Accessed 4 August 2022.
  12. Chelani AB, Devotta S. Air quality assessment in Delhi: Before and after CNG as fuel. Environmental Monitoring and Assessment. 2007;125:257–263. doi: 10.1007/s10661-006-9517-x. [DOI] [PubMed] [Google Scholar]
  13. Chinnaswamy AK. Air pollution in Bangalore, India: An eight-year trend analysis. International Journal of Environmental Technology and Management. 2016;19:177–197. doi: 10.1504/IJETM.2016.082233. [DOI] [Google Scholar]
  14. Das, M., Das, A., Sarkar, R., Saha, S., Mandal, P. (2021). Regional scenario of air pollution in lockdown due to COVID-19 pandemic: Evidence from major urban agglomerations of India. Urban Climate, 37. 10.1016/2Fj.uclim.2021.100821 [DOI] [PMC free article] [PubMed]
  15. Deng LQ. Pollution characteristics of atmospheric particulates in Chengdu from August to September in 2009 and their relationship with meteorological conditions. China Environmental Science. 2012;32:1433–1438. [Google Scholar]
  16. Dholakia HH, Bharda D, Garg A. Short term association between ambient air pollution and mortality and modification by temperature in five Indian cities. Atmospheric Environment. 2014;99:168–174. doi: 10.1016/j.atmosenv.2014.09.071. [DOI] [Google Scholar]
  17. Dutta S, Ghosh S, Dinda S. Urban air-quality assessment and inferring the association between different factors: A comparative study among Delhi, Kolkata and Chennai megacity of India. Aerosol Sci Eng. 2021;5:93–111. doi: 10.1007/s41810-020-00087-x. [DOI] [Google Scholar]
  18. Eregowda, T., Chatterjee, P., Pawar, D. S. (2021). Impact of lockdown associated with COVID19 on air quality and emissions from transportation sector: Case study in selected Indian metropolitan cities. Environment Systems and Decisions, 1–12. 10.1007/s10669-021-09804-4 [DOI] [PMC free article] [PubMed]
  19. Galindo N, Varea M, Molto GJ, Yubero E, Nicolas J. The influence of meteorology on particulate matter concentrations at an urban mediterranean location. Water Soil and Air Pollution. 2011;215:365–372. doi: 10.1007/s11270-010-0484-z. [DOI] [Google Scholar]
  20. Ganguly R, Sharma D, Kumar P. Short-term impacts of air pollutants in three megacities of India during COVID-19 lockdown. Environment, Development and Sustainability. 2021;23:18204–18231. doi: 10.1007/s10668-021-01434-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Gao, B., Ouyang, W., Cheng, H., Xu, Y., Lin, C., Chen, J. (2019). Interactions between rainfall and fine particulate matter investigated by simultaneous chemical composition measurements in downtown Beijing. Atmospheric Environment, 218. 10.1016/j.atmosenv.2019.117000
  22. Gautam S. The influence of COVID-19 on air quality in India: A boon or inutile. Bulletin of Environmental Contamination and Toxicology. 2020;104:724–726. doi: 10.1007/s00128-020-02877-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Geetha, P., Kokila, M. (2015). Estimation of air pollution using remote sensing technique in coimbatore-A case study. International Conference on Communications and Signal Processing (ICCSP), 794–798. 10.1109/ICCSP.2015.7322601.
  24. GOI. (2020). Detail question and answers on COVID-19 for public. https://www.mohfw.gov.in/pdf/Mindinggourmindsduringcoronaeditedat.pdf. Accessed 17 October 2021.
  25. Gouda KC, Singh P, Benke M, Kumari R, et al. Assessment of air pollution status during COVID-19 lockdown (March–May 2020) over Bangalore City in India. Environmental Monitoring and Assessment. 2021;193:1–13. doi: 10.1007/s10661-021-09177-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Government of Tamil Nadu. (2021). coimbatore district https://coimbatore.nic.in/. Accessed 23 October 2021.
  27. Government of Telangana. (2021). Hyderabad district https://hyderabad.telangana.gov.in/. Accessed 23 October 2021.
  28. Gummeneni S, Yusup YB, Chavali M, Samadi SZ. Source apportionment of particulate matter in the ambient air of Hyderabad city, India. Atmospheric Research. 2011;101:752–764. doi: 10.1016/j.atmosres.2011.05.002. [DOI] [Google Scholar]
  29. Guttikunda SK, Kopakka RV. Source emissions and health impacts of urban air pollution in Hyderabad, India. Air Quality, Atmosphere and Health. 2014;7:195–207. doi: 10.1007/s11869-013-0221-z. [DOI] [Google Scholar]
  30. Guttikunda SK, Goel R, Pant P. Nature of air pollution, emission sources, and management in the Indian cities. Atmospheric Environment. 2014;95:501–510. doi: 10.1016/j.atmosenv.2014.07.006. [DOI] [Google Scholar]
  31. Guttikunda SK, Nishadh KA, Jawahar P. Air pollution knowledge assessments (APnA) for 20 Indian cities. Urban Climate. 2019;27:124–141. doi: 10.1016/j.uclim.2018.11.005. [DOI] [Google Scholar]
  32. Harish, M. (2012). A study on air pollution by automobiles in Bangalore city. Management Research and Practice, 4:25–36. http://mrp.ase.ro/no43/f3.pdf
  33. Hemamalini, R. R., Vinodhini, R., Shanthini, B., Partheeban, P. et al. (2022). Air quality monitoring and forecasting using smart drones and recurrent neural network for sustainable development in Chennai City. Sustainable Cities and Society, 85. 10.1016/j.scs.2022.104077
  34. IQAir. (2020a). 2020a World air quality report https://www.greenpeace.org/static/planet4-romania-stateless/2021/03/d8050eab-20-world_air_quality_report.pdf. Accessed 19 October 2021.
  35. IQAir. (2020b). Air quality in India https://www.iqair.com/in-en/india. Accessed 19 October 2021.
  36. IQAir. (2020c). World’s most polluted countries 2020c (PM2.5) https://www.iqair.com/world-most-polluted-countries. Accessed 19 October 2021.
  37. Jacob DJ, Winner DA. Effect of climate change on air quality. Atmospheric Environment. 2009;43(1):51–63. doi: 10.1016/j.atmosenv.2008.09.051. [DOI] [Google Scholar]
  38. Jain S, Sharma T. Social and travel lockdown impact considering coronavirus disease (COVID-19) on air quality in megacities of India: Present benefits, future challenges and way forward. Aerosol and Air Quality Research. 2020;20:1222–1236. doi: 10.4209/aaqr.2020.04.0171. [DOI] [Google Scholar]
  39. Kota SH, Guo H, Myllyvirta L, Hu J, Sahu SK, et al. Year-long simulation of gaseous and particulate air pollutants in India. Atmospheric Environment. 2018;180:244–255. doi: 10.1016/j.atmosenv.2018.03.003. [DOI] [Google Scholar]
  40. Kumar, P., Hama, S., Omidvarborna, H., Sharma, A. et al. (2020). Temporary reduction in fine particulate matter due to ‘anthropogenic emissions switch-off’during COVID-19 lockdown in Indian cities. Sustainable Cities and Society, 62. 10.1016/j.scs.2020.102382 [DOI] [PMC free article] [PubMed]
  41. Kuttippurath J, Patel VK, Pathak M, Singh A. Improvements in SO2 pollution in India: Role of technology and environmental regulations. Environmental Science and Pollution Research. 2022;29:78637–78649. doi: 10.1007/s11356-022-21319-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Laxmipriya S, Narayanan RM, Malarvizhi C, Mageswari W, Kasinathapandian P. Estimation of air pollutant dispersion levels using AERMOD: A case study in Ambattur, Chennai, India. ECS Transactions. 2022;107:13647–13652. doi: 10.1149/10701.13647ecst. [DOI] [Google Scholar]
  43. Li Y, Cheng Q, Zhao H, Wang L, Tao R. Variations in PM10, PM2.5 and PM1.0 in an urban area of the sichuan basin and their relation to meteorological factors. Atmosphere. 2015;6:150–163. doi: 10.3390/atmos6010150. [DOI] [Google Scholar]
  44. Liu Y, Zhou Y, Lu J. Exploring the relationship between air pollution and meteorological conditions in China under environmental governance. Scientific Reports. 2020;10(1):1–11. doi: 10.1038/s41598-020-71338-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Madhavan S, Meenambal T. Monitoring of particulate air pollution due to vehicular emission in Coimbatore city using GIS. Nature Environment and Pollution Technology. 2010;9:43–48. [Google Scholar]
  46. Mahato, S., Pal, S., Ghosh, K. G. (2020). Effect of lockdown amid COVID-19 pandemic on air quality of the megacity Delhi, India. Science of the total environment, 730. 10.1016/j.scitotenv.2020.139086 [DOI] [PMC free article] [PubMed]
  47. Manju N, Balakrishnan R, Mani N. Assimilative capacity and pollutant dispersion studies for the industrial zone of Manali. Atmospheric Environment. 2002;36:3461–3471. doi: 10.1016/S1352-2310(02)00306-0. [DOI] [Google Scholar]
  48. Manju A, Kalaiselvi K, Dhananjayan V, Palanivel M, et al. Spatio-seasonal variation in ambient air pollutants and influence of meteorological factors in Coimbatore, Southern India. Air Quality Atmosphere and Health. 2018;11:1179–1189. doi: 10.1007/s11869-018-0617-x. [DOI] [Google Scholar]
  49. Meda BNM, Mathew A. Temporal variation analysis, impact of COVID-19 on air pollutant concentrations, and forecasting of air pollutants over the cities of Bangalore and Delhi in India. Arabian Journal of Geosciences. 2022;15:736. doi: 10.1007/s12517-022-09996-2. [DOI] [Google Scholar]
  50. Mohanraj R, Azeez PA. Urban development and particulate air pollution in Coimbatore city, India. International Journal of Environmental Studies. 2005;62:69–78. doi: 10.1080/0020723042000261713. [DOI] [Google Scholar]
  51. Moorthy V, Restrepo AMH, Preziosi MP, Swaminathan S. Data sharing for novel coronavirus (COVID-19) Bulletin of the World Health Organization, World Health Organization. 2020;98:150. doi: 10.2471/BLT.20.251561. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Nagendra SMS, Venugopal K, Jones SL. Assessment of air quality near traffic intersections in Bangalore city using air quality indices. Transportation Research Part D: Transport and Environment. 2007;12:167–176. doi: 10.1016/j.trd.2007.01.005. [DOI] [Google Scholar]
  53. Pant G, Alka GD. Air quality assessment among populous sites of major metropolitan cities in India during COVID-19 pandemic confinement. Environmental Science and Pollution Research. 2020;27:44629–44636. doi: 10.1007/s11356-020-11061-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Partheeban, P., Raju, P. H., Hemamailini, R. R., Shanthini, B. (2020). Real-time vehicular air quality monitoring using sensing technology for Chennai. In Transportation Research, Proceedings of CTRG 2017, 19–28. Springer. 10.1007/978-981-32-9042-6
  55. Possanzini M, Buttini P, Palo VD. Characterization of a rural area in terms of dry and wet deposition1988. Science of the Total Environment. 1988;74:111–120. doi: 10.1016/0048-9697(88)90132-5. [DOI] [PubMed] [Google Scholar]
  56. Prather MJ, Hsu J, DeLuca NM, Jackman CH, et al. Measuring and modeling the lifetime of nitrous oxide including its variability. J Geophys Res Atmos. 2015;11:5693–5705. doi: 10.1002/2015JD023267. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Rathore DS, Nagda C, Shaktawat BS, et al. COVID-19 lockdown: A boon in boosting the air quality of major Indian Metropolitan Cities. Aerobiologia. 2021;37:79–103. doi: 10.1007/s10453-020-09673-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Ravindra K, Singh T, Biswal A. Impact of COVID-19 lockdown on ambient air quality in megacities of India and implication for air pollution control strategies. Environmental Science and Pollution Research. 2021;28:21621–21632. doi: 10.1007/s11356-020-11808-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Sahoo PK, Mangla S, Pathak AK, et al. Pre-to-post lockdown impact on air quality and the role of environmental factors in spreading the COVID-19 cases-a study from a worst-hit state of India. International Journal of Biometeorology. 2021;65:205–222. doi: 10.1007/s00484-020-02019-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. SANDRP. (2020). District wise rainfall in India. Retrieved from South Asia Network on Dams, Rivers and People: https://sandrp.in/2020/06/30/district-wise-rainfall-in-india-in-june-2020/ Accessed 5 August 2022.
  61. Sarkar M, Das A, Mukhopadhyay S. Assessing the immediate impact of COVID-19 lockdown on the air quality of Kolkata and Howrah, West Bengal, India. Environment, Development and Sustainability. 2021;23:8613–8642. doi: 10.1007/s10668-020-00985-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Schroeder JR, Crawford JH, Fried A, Walega J, Weinheimer A, Wisthaler A, et al. New insights into the column CH2O/NO2 ratio as an indicator of near-surface ozone sensitivity. Journal of Geophysical Research: Atmospheres. 2017;122:8885–8907. doi: 10.1002/2017JD026781. [DOI] [Google Scholar]
  63. Senthilnathan T. Measurements of urban ambient air quality of Chennai City. Indian Journal of Air Pollution Control. 2008;8:35–47. [Google Scholar]
  64. Singh RP, Chauhan A. Impact of lockdown on air quality in India during COVID-19 pandemic. Air Quality, Atmosphere & Health. 2020;13:921–928. doi: 10.1007/s11869-020-00863-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Singh AK, Gupta HK, Gupta K, Singh P, et al. A comparative study of air pollution in Indian cities. Bulletin of Environmental Contamination and Toxicology. 2007;78:411–416. doi: 10.1007/s00128-007-9220-9. [DOI] [PubMed] [Google Scholar]
  66. Singh, J., Tyagi, B. (2021). Transformation of air quality over a coastal tropical station Chennai during COVID-19 lockdown in India. Aerosol and Air Quality Research, 21. 10.4209/aaqr.200490
  67. Srivastava S, Kumar A, Bauddh K, et al. 21-day lockdown in india dramatically reduced air pollution indices in Lucknow and New Delhi, India. Bulletin of Environmental Contamination and Toxicology. 2020;105:9–17. doi: 10.1007/s00128-020-02895-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. The Power Project, NASA prediction of worldwide energy resources, National aeronautics and Space administration (NASA) (https://power.larc.nasa.gov/data-access-viewer/
  69. Tian G, Qiao Z, Xu X. Characteristics of particulate matter (PM10) and its relationship with meteorological factors during 2001–2012 in Beijing. Environmental Pollution. 2014;192:266–274. doi: 10.1016/j.envpol.2014.04.036. [DOI] [PubMed] [Google Scholar]
  70. UNEP. (2022). Air pollution action note – Data you need to know. Retrieved from United Nations Environment Programme: https://www.unep.org/interactive/air-pollution-note/ Accessed 3 August 2022.
  71. Velmurugan S, Reddy TS. Traffic operating characteristics and its impacts on air pollution in an urban area-a case study of Chennai, India. In Proceedings of the Eastern Asia Society for Transportation Studies. 2005;5:1799–1814. [Google Scholar]
  72. Vijayanand C, Rajaguru P, Kalaiselvi K, Selvam KP, Palanivel M. Assessment of heavy metal contents in the ambient air of the Coimbatore city, Tamilnadu, India. Journal of Hazardous Materials. 2008;160:548–553. doi: 10.1016/j.jhazmat.2008.03.071. [DOI] [PubMed] [Google Scholar]
  73. Wang, Q., Su, M. (2020). A preliminary assessment of the impact of COVID-19 on environment – A case study of China. Science of the Total Environment, 728. 10.1016/j.scitotenv.2020.138915 [DOI] [PMC free article] [PubMed]
  74. Xu X, Zhang Z, Bao L, Mo L, et al. Influence of rainfall duration and intensity on particulate matter removal from plant leaves. Science of the Total Environment. 2017;609:11–16. doi: 10.1016/j.scitotenv.2017.07.141. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Not applicable.

Articles from Environmental Monitoring and Assessment are provided here courtesy of Nature Publishing Group

RESOURCES