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Published in final edited form as: Sci Total Environ. 2021 Jan 18;769:144566. doi: 10.1016/j.scitotenv.2020.144566

Impacts of El Niño-Southern Oscillation on surface dust levels across the world during 1982–2019

Jing Li 1, Eric Garshick 2,3, Shaodan Huang 1,*, Petros Koutrakis 1
PMCID: PMC7969440  NIHMSID: NIHMS1675511  PMID: 33485198

Abstract

Dust pollution has become a significant concern worldwide. Both human activities and climate conditions affect dust levels. This study investigates the influence of El Niño–Southern Oscillation (ENSO), an important large-scale climate phenomenon, on surface dust levels in different regions. We used surface dust concentrations from Retrospective analysis for Research and Applications version 2 reanalysis and Southern Oscillation index (SOI) as dust and ENSO indicators, respectively. First, we first described characteristics of the global surface dust concentrations spanning a period of 37 years (1982–2019). Subsequently, we investigated the associations between monthly surface dust concentrations and SOI in regions with relative high dust levels, (i.e., North Africa, Northwest China and Mongolia, the Middle East, and South Australia) using time-series generalized additive models, controlled for meteorological variables and normalized difference vegetation index (NDVI). In order to capture the delayed effects of ENSO on dust, we fitted the model for SOI with 13 different moving averages starting from 12 months before. The highest average surface dust concentration for our study regions was 306.68 μg/m3, observed in North Africa. The average dust concentrations in the Middle East, Northwest China, and South Australia were 193.18, 113.64, and 77.19 μg/m3, respectively. Our results showed that dust concentrations were positively related with SOI. The associations between dust and SOI were more significant and higher for North Africa and the Middle East. Our results indicated that for regions with high dust pollution, La Niña episodes are associated with increased dust concentrations, while El Niño events are associated with decreased dust concentrations.

Keywords: El Niño–Southern Oscillation (ENSO), Southern Oscillation index (SOI), dust, climate change

1. Introduction

Dust particles are the dominant components of atmospheric aerosols in many regions worldwide (Bryant et al., 2007;Neff et al., 2008;Zhao et al., 2010;Lee and Sohn, 2011;Zhang et al., 2016). Severe dust events greatly affect air quality, climate conditions, and human health (Goudie, 2014;Ganor et al., 2010;Khaniabadi et al., 2017;Ashrafi et al., 2017). Dust particles have benn found to affect visibility (Giannadaki et al., 2014), wind circulation and moisture fluxes (Alpert et al., 1998;Fuzzi et al., 2015). Dust events have also been positively associated with mortality and the hospital admissions (Perez et al., 2008;Kang et al., 2012;Thalib and Al-Taiar, 2012;Neophytou et al., 2013;Vodonos et al., 2014;Crooks et al., 2016;Trianti et al., 2017). Contributions to high dust levels include increased emissions, fand dry climate condition. On theglobal scale, the contribution from natural sources is higher than the anthropogenic ones (Ginoux et al., 2012). Dust pollution may be most affected by climate variations (Fuzzi et al., 2015;Parolari et al., 2016). Therefore, it is worthwhile to investigate the influences of large-scale climate variations on dust levels in different areas worldwide, particularly in arid and semiarid regions.

El Niño–Southern Oscillation (ENSO), one of the most important phenomena in the Earth’s climate system, is a complex ocean-atmospheric interaction in the tropical Pacific Ocean, (Lv et al., 2019). ENSO events can be classified into the following three categories: warm events (El Niño), cool events (La Niña), and neutral conditions, based on sea surface temperature (SST) anomalies in the Pacific Ocean. Several associated indicators are used to describe the characteristics of ENSO, e.g., SST, Southern Oscillation Index (SOI) and the Multivariate ENSO Index (MEI) (Zhu et al., 2019). ENSO events have exerted enormous influence on the climate globally. Previous studies have found that ENSO can cause extreme hydrological events (Veldkamp et al., 2015), such as floods (Ward et al., 2014) and droughts (Zhang et al., 2015), which have socioeconomic and environmental impacts (Fasullo et al., 2018;He et al., 2019). Many studies have also linked ENSO to precipitation, wind speed, and temperature at global and regional scales (Sun et al., 2017;Moore, 2019;Kim et al., 2015;Bruick et al., 2019). As ENSO is a slowly evolving climate phenomenon, the peak of ENSO can be predicted a few months in advance (Kingaman and Keat, 2018). Therefore, understanding its global effects is crucial in providing early warning advice to vulnerable regions.

Previous studies indicate that: dust levels are largely influenced by regional meteorology and land surface conditions (Namdari et al., 2018), while ENSO substantially modulates precipitation, wind circulation, and temperature (Sun et al., 2017;Moore, 2019;Kim et al., 2015;Bruick et al., 2019). While it is likely that dust levels are associated with global disturbances caused by ENSO, there has been a lack of confirming scientific evidence.

This study aims to provide a better understanding the association between dust levels and ENSO. We used monthly surface dust concentration and SOI as indexes for dust and ENSO, respectively. We reviewed the characteristics of global monthly surface dust concentrations over a period of 37 years (1982–2019). Subsequently, we investigated the potential influence of ENSO on monthly surface dust concentrations in the regions with highest dust concentrations using a time-series generalized additive model (GAM), adjusted for meteorological variables and normalized difference vegetation index (NDVI).

2. Material and Methods

2.1. Study Domain

This is a global scale study spanning from January 1982 to June 2019 with a monthly temporal resolution. We investigated the relationship between ENSO and dust in regions with the highest dust levels.

2.2. Data collection

2.2.1. Dust index

The “Dust Surface Mass Concentration” of Retrospective analysis for Research and Applications version 2 (MERRA-2) was used as an index to determine the levels of dust pollution for the study regions (https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/). MERRA-2 is a reanalysis data provide by the U.S. National Aeronautics and Space Administration (NASA), which are estimated using satellite measurements from the Goddard Earth Observing System Model, Version 5 (Veselovskii et al., 2018). This data is in excellent agreement with the results of other satellite observations and those of ground-based measurements (Rienecker et al., 2011). The spatial resolution of monthly MERRA-2 estimates is 0.625° (longitude) × 0.5° (latitude).

2.2.2. ENSO index

We used SOI to characterize the intensites of El Niño and La Niña. SOI is a standardized index computed as the normalized difference of the sea level pressure at Tahiti (T) and Darwin (D), Australia. Prolonged periods of negative SOI values strongly correspond to abnormally warm ocean waters across the eastern tropical Pacific typical of El Niño episodes, and prolonged periods of positive SOI values coincide with typical La Niña episodes (Ashouri, 2008;Zhu et al., 2019). The monthly SOI values are available from the National Oceanic and Atmospheric Administration Climate Prediction Center (https://www.cpc.ncep.noaa.gov/data/indices/soi/). Monthly SOI values for our study periods are shown in Figure 1. Table S1 lists El Niño and La Niña events from 1982–2019 (Zhu et al., 2019).

Figure 1.

Figure 1.

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Monthly SOI values from 1982–2019 (https://www.cpc.ncep.noaa.gov/data/indices/soi/).

2.2.3. Metrological data and NDVI

Metrological variables and NDVI were used as covariates in our regression analysis. Meteorological data were obtained from MERRA-2, which have the same spatial and temporal resolution as the dust index. Metrological variables included wind speed, temperature, surface pressure, total precipitation, planetary boundary layer height, and relative humidity. The NDVI values are acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensor: (https://climatedataguide.ucar.edu/climate-data/ndvi-normalized-difference-vegetation-index-noaa-avhrr). The data are collected daily with a spatial resolution of 0.5° (longitude) ×0.5° (latitude).

2.3. Statistic model

For our studied regions, we applied a time-series GAM model to investigate the associations between surface dust concentration and SOI from January 1982 to June 2019. In order to capture the immediate and delayed effects of ENSO on surface dust concentration, we fitted the model for SOI with different moving averages starting from the current month (month0) up to 12 months before (month12). We controlled for: 1) long-time trends using natural cubic splines with 2 degree of freedom (df); 2) current monthly averages (month0) of NDVI and metrological variables with natural cubic splines with 2df, and; 3) month of the year as a categorical variable. The equation for the model is shown below, Eq (1):

E(Di)=β0+β1×f(SOI)+β2×month+s(NDVI)+s(WDSP)+s(TMP)+s(PRE)+s(PBLH)+s(time) Eq. (1)

where E(Di) is the average monthly surface dust concentration for each region; β0 is the y intercept; SOI is the SOI value for different moving averages starting from month0 to month12; month is the month of year; s(NDVI), s(WSPD), s(TMP), s(PRE), and s(PBLH) are natural splines with 2 df for monthly NDVI, wind speed, temperature, total precipitation, and planetary boundary layer height for each region, respectively; s(time) is the smooth function of time with 2 df.

A total of 450 monthly observations were used in the model for each region. All statistical analyses were performed using R software 3.5.0. The time-series GAM models were fitted using the “mgcv” package. We considered the effect as significant when p < 0.05 and as marginally significant when 0.05 ≤ p < 0.1. The variable selection and general results of the models are described in Text S1.

3. Results

3.1. Surface dust concentrations

Figure 2a displays the spatial distributions of the average surface dust concentrations for the entire study period. The average surface dust concentrations of each grid cell during the study period were regressed on year to assess the time trends (Figure 2b).

Figure 2.

Figure 2.

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Average (a, unit: μg/m3) and time trend (b, unit: μg/m3· year) of global surface dust concentrations for the study period.

We fitted a linear model (dusti=β0+β1×year) for each grid cell to assess the time trend (β1). Figure 2b shows the values of β1 for each grid.

Because the highest levels of surface dust concentration had been observed for these regions, we investigated the impact of ENSO on dust levels in North Africa (NA), the Middle East (ME), Northwest China and Mongolia (NC), and South Australia (SA). The latitude and longitude range of each study region is shown in Table S1. Table 1 presents the descriptive statistics of the surface dust concentration in each region during the study period. The highest average surface dust concentration, 306.68 μg/m3, was observed in NA. The average dust concentrations in ME, NC, and SA are 193.18, 113.64, and 77.19 μg/m3, respectively. Figure 3 shows the average surface dust concentrations by year and month for each region during 1982–2019. Dust levels in different regions exhibited different seasonal patterns. The surface dust concentration in NA was very high throughout the year. For ME, the highest dust levels were observed in June and July (summer), and the lowest levels were observed in October to December (winter). In NC, the dust levels in April, and May were much higher than the other months. For SA, the dust levels in May, June, and July were much lower than the other months.

Table 1.

Descriptive statistics of monthly dust concentration (μg/m3) in each region throughout the study period.

Region Mean SD Maximum Minimum
Northern Africa (NA) 306.68 40.60 430.67 204.05
The Middle East (ME) 193.18 42.92 340.90 97.21
Northwest China and Mongolia (NC) 113.64 28.65 212.08 51.42
South Australia (SA) 77.19 21.19 129.00 29.15
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Figure 3.

Figure 3.

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Average surface dust concentrations (μg/m3) by month and by year in each region from 1982–2018 (Blue line: smoothed using a linear model).

3.2. Impacts of ENSO on dust concentrations

The correlation coefficients (β1) between the monthly surface dust concentration for each grid cell and the monthly global SOI value during 1982–2019 are displayed in Figure S1. We assessed the associations between monthly dust concentration and SOI for 13 different moving averages starting from 12 months before for each region. The regression coefficients of SOI for different moving averages from month0 (the average value for the current month) to month12 (average value from 12 month before) for each region are listed in Table S3. The changes of dust concentration (ug/m3) with per unit SOI for different moving averages for each region are shown in Figure 4.

Figure 4.

Figure 4.

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Associations between change of dust concentrations with unit SOI for different lag months in each region.

Our results indicated that dust concentrations are positively associated with SOI. Figure 4 shows the results, with more significant and higher associations for NA and ME. For NA, we found significantly positive associations between dust and SOI for moving averages from month0 to month12, with dust changes per unit SOI ranged from 3.93μg/m3 (95%CI: 2.05 μg/m3, 5.81 μg/m3) to 6.30μg/m3 (95%CI: 3.82 μg/m3, 8.79 μg/m3). For ME, we found significantly positive associations between dust and SOI for moving averages from month1 to month12. Dust changes with per unit SOI ranged from 2.14 μg/m3 (95%CI: −0.01 μg/m3, 4.30 μg/m3) to 4.07 μg/m3 (95%CI: 1.47 μg/m3, 6.66 μg/m3). Although we did not find significant associations for these values for NC and SA, the patterns are almost all positive (except for month0 and month1 in NC). The dust changes per unit SOI for NC and SA, respectively, ranged from −0.43 μg/m3 (95%CI: −2.19 μg/m3, 1.33 μg/m3) to 1.73 μg/m3 (95%CI: −0.38 μg/m3, 3.85 μg/m3) and from −0.70 μg/m3 (95%CI: −0.32 μg/m3, 1.73 μg/m3) to 0.87 μg/m3 (95%CI: −0.37 μg/m3, 2.11 μg/m3).

Figure 4 shows that dust changes per unit SOI are different for different moving averages for NA, ME and NC. For NA and ME, dust changes increased with moving averages until month3 and then became stable. In NC, dust changes increased with moving averages until month7 and showed no further increase for higher moving averages. However, in SA, the dust changes with moving averages are negligible.

4. Discussion

Using a time-series GAM, we investigated the associations between monthly global SOI and surface dust concentrations at a spatial resolution of 0.625° (longitude) × 0.5° (latitude) in regions with high dust concentrations including North Africa, the Middle East, Northwest China and Mongolia, and South Australia. We captured the delay of effects of SOI by fitting the model for SOI with different moving average starting from current month to 12 months before.

Our results show that dust concentrations were positively correlated with SOI. The associations between dust and SOI were more significant and larger for regions with higher dust concentrations (North Africa and the Middle East). Negative (low) SOI values correspond to El Niño events, and positive (high) SOI values coincide with typical La Niña events (Ashouri, 2008;Zhu et al., 2019). Thus, our results indicated that the La Niña episodes would increase dust concentrations, and El Niño events would decrease dust concentrations for regions with high dust pollution.

The dust changes with SOI were different for different moving averages. In North Africa, the Middle East, Northwest China, the dust changes increased with moving averages until month3, month7, and month7, respectively, and then became stable. In South Australia, the dust changes with different moving averages were negligible, indicating that the effect of month0 is sufficient to characterize the association. Because SOI is computed based on the difference of the sea level pressure in Tahiti (T) and Darwin (D), Australia, which are located in the Pacific Ocean. Therefore, in regions closer to Pacific (e.g., South Australia) the effect of SOI in the current month was adequate to characterize the association. In agreement with previous results (Kingaman and Keat, 2018), dust changes per unit SOI in regions that are more remote from the Pacific Ocean took longer to get to maximum values.

The results of existing studies of the relationship between air pollution and ENSO at regional scales are similar to ours. According to Banerjee and Kumar (2016), in the region around the Northwest Indian Ocean, La Niña leads to more dust generation during the following summer, while El Niño is responsible for the opposite. He et al. (2019) found that ENSO was positively associated with winter haze days in North China. Kim et al. (2015) found that La Niña favoring increased aerosol over northern India.

Our study has some limitations. ENSO is a very complex climatic phenomenon. Although SOI is strong correlated with the intense of ENSO events (Ashouri, 2008;Zhu et al., 2019), it cannot be used to completely characterize ENSO events. In addition, some confounders which are not included in our model may have influenced our findings for on ENSO-dust relationships. The above two limitations have introduced bias and uncertainties in our results.

The advantages of our study include: 1) to the best of our knowledge, this is the first study to investigate how dust concentrations in the world directly respond to ENSO events; 2) we used the long-term dust concentrations data from MERRA-2 with high accuracy and spatial resolution to capture the distribution of global dust levels; 3) we controlled for meteorological variables and NDVI to better assure that our model is predictive.

5. Conclusions

This study investigated the impacts of ENSO events on dust concentrations in different regions of the world during 1982–2018. Our results showed that dust concentrations were positively associated with SOI (as a surrogate for ENSO) for regions with high dust pollution.. These results are an important contribution to our understanding of the influence of ENSO on dust concentrations.

Supplementary Material

supplement

Acknowledgments

This work was supported in part by the VA Cooperative Studies Program #595: Respiratory Health and Deployment to Iraq and Afghanistan, from the United States Department of Veterans Affairs, Office of Research and Development, Clinical Science Research and Development, Cooperative Studies Program. This publication was also supported by U.S. Environmental Protection Agency (EPA) grant RD-835872. The contents do not represent the views of the U.S. Department of Veterans Affairs, U.S. EPA or U.S. Government. The computations in this paper were run on the Odyssey cluster of Harvard University. The authors appreciate Dr. Jack M. Wolfson for proofreading the article.

Footnotes

Declaration of Competing Interest

There are no competing financial interests for the authors of this paper.

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