An overview of regional and local characteristics of aerosols in South Africa using satellite, ground, and modeling data

Ensemble averaging using remote sensing data to model spatiotemporal PM10 concentrations in sparsely monitored South Africa

There is a paucity of air quality data in sub-Saharan African countries to inform science driven air quality management and epidemiological studies. We investigated the use of available remote-sensing aerosol optical depth (AOD) data to develop spatially and temporally resolved models to predict daily particulate matter (PM₁₀) concentrations across four provinces of South Africa (Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape) for the year 2016 in a two-staged approach. In stage 1, a Random Forest (RF) model was used to impute Multiangle Implementation of Atmospheric Correction AOD data for days where it was missing. In stage 2, the machine learner algorithms RF, Gradient Boosting and Support Vector Regression were used to model the relationship between ground-monitored PM₁₀ data, AOD and other spatial and temporal predictors. These were subsequently combined in an ensemble model to predict daily PM₁₀ concentrations at 1 km × 1 km spatial resolution across the four provinces. An out-of-bag R² of 0.96 was achieved for the first stage model. The stage 2 cross-validated (CV) ensemble model captured 0.84 variability in ground-monitored PM₁₀ with a spatial CV R² of 0.48 and temporal CV R² of 0.80. The stage 2 model indicated an optimal performance of the daily predictions when aggregated to monthly and annual means. Our results suggest that a combination of remote sensing data, chemical transport model estimates and other spatiotemporal predictors has the potential to improve air quality exposure data in South Africa's major industrial provinces. In particular, the use of a combined ensemble approach was found to be useful for this area with limited availability of air pollution ground monitoring data.

The impact of meteorological parameters on PM10 and visibility during the Middle Eastern dust storms

Air pollution is one of the most pressing issues in populated Middle Eastern cities, in particular for the city of Ahvaz, Iran, imposing deleterious effects on the environment, public health, economy, culture, and other sectors. In this study, we investigate the relationship between meteorological parameters, PM₁₀, AOD, air mass source origin, and visibility during severe desert dust storms (Average_3h PM₁₀ > 3200 µg m^-3) between 2009 and 2012. Six of seven such events occurred between February and March. Interestingly, for the seven cases there was always an alarming PM₁₀ mass concentration peak (137-553 µg m^-3) between 12:00-18:00 (local time) that was 18-24 h before the dominant peak of the storm (3279-4899 µg m^-3). The maximum wind speed over the multi-day periods examined for the dust storms is usually observed 6 h before the alarming PM₁₀ peak. The minimum relative humidity, dew point temperature and air pressure occurred ± 3 h around the time of the alarming PM₁₀ peak. Wind speed was the meteorological parameter that was consistently higher around the time of the first peak as compared to the second peak, with the reverse being true for sea level pressure. Based on four years of daily data in Ahvaz, PM₁₀ was positively correlated with wind speed and air temperature and inversely correlated with sea level pressure and RH. An empirically-derived equation with R² = 0.95 is reported to estimate the maximum PM₁₀ concentration for severe desert dust events in the study region based on meteorological parameters. Finally, AOD is shown to correlate strongly (R² = 0.86) with PM₁₀ during periods with severe desert dust storms in the region.

Air Pollution Dispersion over Durban, South Africa

Air pollution dispersion over Durban is studied using satellite, reanalysis and in situ measurements. This coastal city of 4 million people located on the east coast of South Africa contributes 29 million T/yr of trace gases, mostly from transport and industry. Terrestrial and agricultural particulates derive from the Kalahari Desert, Zambezi Valley and Mozambique. Surface air pollutants accumulate during winter (May–August) and provide a focus for statistical analysis of monthly, daily and hourly time series since 2001. The mean diurnal cycle has wind speed minima during the land−sea breeze transitions that follow morning and evening traffic emissions. Daily air pollution concentrations (CO, NO2, O3, PM2.5 and SO2) vary inversely with dewpoint temperature and tend to peak during winter prefrontal weather conditions. Descending airflow from the interior highlands induces warming, drying and poor air quality, bringing dust and smoke plumes from distant sources. Spatial regression patterns indicate that winters with less dispersion are preceded by warm sea surface temperatures in the tropical West Indian Ocean that promote a standing trough near Durban. Statistical outcomes enable the short- and long-range prediction of atmospheric dispersion and risk of exposure to unhealthy trace gases and particulates. The rapid inland decrease of mean wind speed from 8 to 2 m/s suggests that emissions near the coast will disperse readily compared with in interior valleys.

Spatial and Temporal Variations in PM10 Concentrations between 2010-2017 in South Africa

Particulate matter less than or equal to 10 μm in aerodynamic diameter (PM₁₀ µg/m³) is a priority air pollutant and one of the most widely monitored ambient air pollutants in South Africa. This study analyzed PM₁₀ from monitoring 44 sites across four provinces of South Africa (Gauteng, Mpumalanga, Western Cape and KwaZulu-Natal) and aimed to present spatial and temporal variation in the PM₁₀ concentration across the provinces. In addition, potential influencing factors of PM₁₀ variations around the three site categories (Residential, Industrial and Traffic) were explored. The spatial trend in daily PM₁₀ concentration variation shows PM₁₀ concentration can be 5.7 times higher than the revised 2021 World Health Organization annual PM₁₀ air quality guideline of 15 µg/m³ in Gauteng province during the winter season. Temporally, the highest weekly PM₁₀ concentrations of 51.4 µg/m³, 46.8 µg/m³, 29.1 µg/m³ and 25.1 µg/m³ at Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape Province were recorded during the weekdays. The study results suggest a decrease in the change of annual PM₁₀ levels at sites in Gauteng and Mpumalanga Provinces. An increased change in annual PM₁₀ levels was reported at most sites in Western Cape and KwaZulu-Natal.

A machine learning model to estimate ambient PM2.5 concentrations in industrialized highveld region of South Africa

Exposure to fine particulate matter (PM_2.5) has been linked to a substantial disease burden globally, yet little has been done to estimate the population health risks of PM_2.5 in South Africa due to the lack of high-resolution PM_2.5 exposure estimates. We developed a random forest model to estimate daily PM_2.5 concentrations at 1 km² resolution in and around industrialized Gauteng Province, South Africa, by combining satellite aerosol optical depth (AOD), meteorology, land use, and socioeconomic data. We then compared PM_2.5 concentrations in the study domain before and after the implementation of the new national air quality standards. We aimed to test whether machine learning models are suitable for regions with sparse ground observations such as South Africa and which predictors played important roles in PM_2.5 modeling. The cross-validation R² and Root Mean Square Error of our model was 0.80 and 9.40 μg/m³, respectively. Satellite AOD, seasonal indicator, total precipitation, and population were among the most important predictors. Model-estimated PM_2.5 levels successfully captured the temporal pattern recorded by ground observations. Spatially, the highest annual PM_2.5 concentration appeared in central and northern Gauteng, including northern Johannesburg and the city of Tshwane. Since the 2016 changes in national PM_2.5 standards, PM_2.5 concentrations have decreased in most of our study region, although levels in Johannesburg and its surrounding areas have remained relatively constant. This is anadvanced PM_2.5 model for South Africa with high prediction accuracy at the daily level and at a relatively high spatial resolution. Our study provided a reference for predictor selection, and our results can be used for a variety of purposes, including epidemiological research, burden of disease assessments, and policy evaluation.

Source apportionment of fine atmospheric particles using positive matrix factorization in Pretoria, South Africa

In Pretoria South Africa, we looked into the origins of fine particulate matter (PM_2.5), based on 1-year sampling campaign carried out between April 18, 2017, and April 17, 2018. The average PM_2.5 concentration was 21.1 ± 15.0 µg/m³ (range 0.7-66.8 µg/m³), with winter being the highest and summer being the lowest. The XEPOS 5 energy dispersive X-ray fluorescence (EDXRF) spectroscopy was used for elemental analysis, and the US EPA PMF 5.0 program was used for source apportionment. The sources identified include fossil fuel combustion, soil dust, secondary sulphur, vehicle exhaust, road traffic, base metal/pyrometallurgical, and coal burning. Coal burning and secondary sulphur were significantly higher in winter and contributed more than 50% of PM_2.5 sources. The HYSPLIT model was used to calculate the air mass trajectories (version 4.9). During the 1-year research cycle, five transportation clusters were established: North Limpopo (NLP), Eastern Inland (EI), Short-Indian Ocean (SIO), Long-Indian Ocean (LIO), and South Westerly-Atlantic Ocean (SWA). Local and transboundary origin accounted for 85%, while 15% were long-range transport. Due to various anthropogenic activities such as biomass burning and coal mining, NLP clusters were the key source of emissions adding to the city's PM rate. In Pretoria, the main possible source regions of PM_2.5 were discovered to be NLP and EI. Effective control strategies designed at reducing secondary sulphur, coal burning, and fossil fuel combustion emissions at Southern African level and local combustion sources would be an important measure to combat the reduction of ambient PM_2.5 pollution in Pretoria.

Comparison of Aerosol Optical Depth from MODIS Product Collection 6.1 and AERONET in the Western United States

Recent observations reveal that dust storms are increasing in the western USA, posing imminent risks to public health, safety, and the economy. Much of the observational evidence has been obtained from ground-based platforms and the visual interpretation of satellite imagery from limited regions. Comprehensive satellite-based observations of long-term aerosol records are still lacking. In an effort to develop such a satellite aerosol dataset, we compared and evaluated the Aerosol Optical Depth (AOD) from Deep Blue (DB) and Dark Target (DT) product collection 6.1 with the Aerosol Robotic Network (AERONET) program in the western USA. We examined the seasonal and monthly average number of Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua DB AOD retrievals per 0.1∘×0.1∘ from January 2003 to December 2017 across the region’s different topographic, climatic, and land cover conditions. The number of retrievals in the southwest United States was on average greater than 37 days per 90 days for all seasons except summer. Springtime saw the highest number of AOD retrievals across the southwest, consistent with the peak season for synoptic-scale dust events. The majority of Arizona, New Mexico, and western Texas showed the lowest number of retrievals during the monsoon season. The majority of collocating domains of AOD from the Aqua sensor showed a better correlation with AERONET AOD than AOD from Terra, and the correlation coefficients exhibited large regional variability across the study area. The correlation coefficient between the couplings Aqua DB AOD-AERONET AOD and Terra DB AOD-AERONET AOD ranges from 0.1 to 0.94 and 0.001 to 0.94, respectively. In the majority of the sites that exhibited less than a 0.6 correlation coefficient and few matched data points at the nearest single pixel, the correlations gradually improved when the spatial domain increased to a 50 km × 50 km box averaging domain. In general, the majority of the stations revealed significant correlation between MODIS and AERONET AOD at all spatiotemporal aggregating domains, although MODIS generally overestimated AOD compared to AERONET. However, the correlation coefficient in the southwest United States was the lowest and in stations from a higher latitude was the highest. The difference in the brightness of the land surface and the latitudinal differences in the aerosol inputs from the forest fires and solar zenith angles are some of the factors that manifested the latitudinal correlation differences.

An Aerosol Climatology and Implications for Clouds at a Remote Marine Site: Case Study Over Bermuda

Aerosol characteristics and aerosol-cloud interactions remain uncertain in remote marine regions. We use over a decade of data (2000-2012) from the NASA AErosol RObotic NETwork, aerosol and wet deposition samples, satellite remote sensors, and models to examine aerosol and cloud droplet number characteristics at a representative open ocean site (Bermuda) over the Western North Atlantic Ocean (WNAO). Annual mean values were as follows: aerosol optical depth (AOD) = 0.12, Ångström Exponent (440/870 nm) = 0.95, fine mode fraction = 0.51, asymmetry factor = 0.72 (440 nm) and 0.68 (1020 nm), and Aqua-MODIS cloud droplet number concentrations = 51.3 cm-3. The winter season (December-February) was characterized by high sea salt optical thickness and the highest aerosol extinction in the lowest 2 km. Extensive precipitation over the WNAO in winter helps contribute to the low FMFs in winter (~0.40-0.50) even though air trajectories often originate over North America. Spring and summer had more pronounced influence from sulfate, dust, organic carbon, and black carbon. Volume size distributions were bimodal with a dominant coarse mode (effective radii: 1.85-2.09 μm) and less pronounced fine mode (0.14-0.16 μm), with variability in the coarse mode likely due to different characteristic sizes for transported dust (smaller) versus regional sea salt (larger). Extreme pollution events highlight the sensitivity of this site to long-range transport of urban emissions, dust, and smoke. Differing annual cycles are identified between AOD and cloud droplet number concentrations, motivating a deeper look into aerosol-cloud interactions at this site.

Major climate change-induced risks to human health in South Africa

There are many climatic changes facing South Africa which already have, or are projected to have, a detrimental impact on human health. Here the risks to health due to several alterations in the climate of South Africa are considered in turn. These include an increase in ambient temperature, causing, for example, a significant rise in morbidity and mortality; heavy rainfall leading to changes in the prevalence and occurrence of vector-borne diseases; drought-associated malnutrition; and exposure to dust storms and air pollution leading to the potential exacerbation of respiratory diseases. Existing initiatives and strategies to prevent or reduce these adverse health impacts are outlined, together with suggestions of what might be required in the future to safeguard the health of the nation. Potential roles for the health and non-health sectors as well as preparedness and capacity development with respect to climate change and health adaptation are considered.

Assessing the Relationship between Economic Growth and Emissions Levels in South Africa between 1994 and 2019

The objective of this study is to establish whether there is any relationship between economic growth and emission levels for pollutants (namely carbon dioxide (CO2), black carbon (BC), sulfur dioxide (SO2), and carbon monoxide (CO)) in South Africa, for the period from 1994 to 2019. Data from the world bank, namely gross domestic product (GDP) and CO2 emissions, were used. BC, SO2, and CO data were obtained from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). The linear correlation coefficient and the environmental Kuznets curve (EKC) hypothesis test were used to determine the relationships. The sequential Mann–Kendall (SQMK) test was further used to study the trends. A correlation coefficient of 0.84, which indicates a strong positive linear correlation, between GDP and CO2 emission was observed. However, the relationship between GDP and CO concentration showed a correlation coefficient of −0.05, indicating no linear relationship between the two variables. The EKC hypothesis showed an N-shape for SO2 and CO. Overall, the results of this study indicate that emissions levels are generally correlated with economic growth. Therefore, a stringent regulatory system is needed to curtail the high emissions levels observed in this study, given the devastating impacts of global warming already ravaging the world.

Contrasting indoor and ambient particulate matter concentrations and thermal comfort in coal and non-coal burning households at South Africa Highveld

One of the key challenges noted in the sustainable development goals for good health and wellbeing (SDGs 3) is both ambient and household air pollution. Household solid fuel combustion represents one of the biggest threat to human health in South Africa. This study helps to understand the impact of solid fuel burning in an indoor and ambient environment. Continuous monitoring of particulate matter (PM₄) was carried out in two houses, one used coal as a primary source of energy, while the other did not. For solid fuel burning (SFB) house the winter PM₄ average 24-h concentration ranges between 60.9 μg m^-3 and 207.5 μg m^-3 while at non-solid fuel burning (NSFB) house it ranges between 15.3 μg m^-3 and 84.2 μg m^-3. In both houses, the national ambient air quality standard (NAAQS) for PM2.5 (40 μg m^-3) were exceeded during winter. The summer PM₄ levels ranged between 17.4 μg m^-3 and 36.6 μg m^-3 in the solid fuel burning house and between 14.2 μg m^-3 and 39.9 μg m^-3 at the non-solid fuel-burning house. During mornings and evenings, indoor concentrations were higher than the outdoor; these periods coincide with the fuel-burning pattern in this community. In the mid-afternoon, the outdoor PM levels sometimes went higher than the indoor levels, perhaps as a result of the pollution from the power plants in the neighbourhood. Using the linear regression model, there were no significant correlations between indoor/outdoor PM₄ concentrations during the winter, but there were good correlations for both houses during the summer. There was an observed difference in the thermal comfort at the SFB and NSFB. The temperature at SFB went below the World Health Organisation standard in winter and above during the summer while at NSFB, the temperature was managed within the standard in both seasons.

Public health benefits of reducing exposure to ambient fine particulate matter in South Africa

Air pollution is a growing problem in developing countries, and there exists a wide range of evidence documenting the large health and productivity losses associated with high concentrations of pollutants. South Africa is a developing country with high levels of air pollution in some regions, and the costs of air pollution on human health and economic growth in South Africa are still uncertain. The environmental Benefits Mapping and Analysis Program (BenMAP) model was applied to South Africa using local data on population, mortality rates, and concentrations of fine particulate matter (PM_2.5), as well as mortality risk coefficients from the epidemiological literature. BenMAP estimates the number of premature deaths that would likely have been avoided if South African air quality levels met the existing annual National Ambient Air Quality Standard (NAAQS) of 20 μg m^-3, and the more stringent World Health Organization (WHO) guideline for annual average PM_2.5 of 10 μg m^-3. We estimate 14,000 avoided premature mortalities in 2012 if all of South Africa met the existing NAAQS annual average standard for PM_2.5. These avoided cases of mortality have an estimated monetary value of $14.0 billion (US2011$), which is equivalent to 2.2% of South Africa's 2012 GDP (PPP, US2011$). We estimate 28,000 avoided premature mortalities if the more stringent WHO guideline for annual average PM_2.5 is met across South Africa, which when expressed as a national burden is equivalent to 6% of all deaths in South Africa being attributable to PM_2.5 exposure. These avoided cases of mortality have an estimated monetary value of $29.1 billion, which is equivalent to 4.5% of South Africa's 2012 GDP. These results show that there are significant public health benefits to lowering PM_2.5 concentrations across South Africa, with correspondingly high economic benefits.

Characterization and Risk Assessment of Atmospheric PM2.5 and PM10 Particulate-Bound PAHs and NPAHs in Rwanda, Central-East Africa

Exposure to airborne particulates is estimated as the largest cause of premature human mortality worldwide and is of particular concern in sub-Saharan Africa where emissions are high and data are lacking. Particulate matter (PM) contains several toxic organic species including polycyclic aromatic hydrocarbons (PAHs) and nitrated PAHs (NPAHs). This study provides the first characterization and source identification for PM₁₀- and PM_2.5-bound PAHs and NPAHs in sub-Saharan Africa during a three-month period that spanned dry and wet seasons at three locations in Rwanda. The 24-h mean PM_2.5 and PM₁₀ concentrations were significantly higher in the dry than the wet season. PAH and NPAH concentrations at the urban roadside site were significantly higher than the urban background and rural site. Source identification using diagnostic ratio analysis and principal component analysis (PCA) revealed diesel and gasoline-powered vehicles at the urban location and wood burning at the rural location as the major sources of PAHs and NPAHs. Our analysis demonstrates that PM concentrations and lifetime cancer risks resulting from inhalation exposure to PM-bound PAHs and NPAHs exceed World Health Organization safe limits. This study provides clear evidence that an immediate development of emission control measures is required.

On the Morphology and Composition of Particulate Matter in an Urban Environment

Particulate matter (PM) plays a vital role in altering air quality, human health, and climate change. There are sparse data relevant to PM characteristics in urban environments of the Middle East, including Peshawar city in Pakistan. This work reports on the morphology and composition of PM in two size fractions (PM2.5 and PM10) during November 2016 in Peshawar. The 24 hous mass concentration of PM2.5 varied from 72 μg m-3 to 500 μg m-3 with an average value of 286 μg m-3. The 24 hours PM10 concentration varied from 300 μg m-3 to 1440 μg m-3 with an average of 638 μg m-3. The morphology, size, and elemental composition of PM were measured using Fourier Transform Infra Red (FT-IR) Spectroscopy and Scanning Electron Microscopy (SEM) with Energy Dispersive X-ray (EDX) Spectroscopy. The size of the analyzed particles by EDX ranged from 916 nm to 22 μm. Particles were classified into the following groups based on their elemental composition and morphology: silica (12%), aluminosilicates (23%), calcium rich (3%), chloride (2%), Fe/Ti oxides (3%), carbonaceous (49%), sulfate (5%), biogenic (3%). The major identified sources of PM are vehicular emissions, biomass burning, soil and re-suspended road dust, biological emissions, and construction activities in and around the vicinity of the sampling site.

Land Use Regression Modelling of Outdoor NO₂ and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa

Air pollution can cause many adverse health outcomes, including cardiovascular and respiratory disorders. Land use regression (LUR) models are frequently used to describe small-scale spatial variation in air pollution levels based on measurements and geographical predictors. They are particularly suitable in resource limited settings and can help to inform communities, industries, and policy makers. Weekly measurements of NO₂ and PM_2.5 were performed in three informal areas of the Western Cape in the warm and cold seasons 2015–2016. Seasonal means were calculated using routinely monitored pollution data. Six LUR models were developed (four seasonal and two annual) using a supervised stepwise land-use-regression method. The models were validated using leave-one-out-cross-validation and tested for spatial autocorrelation. Annual measured mean NO₂ and PM_2.5 were 22.1 μg/m³ and 10.2 μg/m³, respectively. The NO₂ models for the warm season, cold season, and overall year explained 62%, 77%, and 76% of the variance (R²). The PM_2.5 annual models had lower explanatory power (R² = 0.36, 0.29, and 0.29). The best predictors for NO₂ were traffic related variables (major roads, bus routes). Local sources such as grills and waste burning sites appeared to be good predictors for PM_2.5, together with population density. This study demonstrates that land-use-regression modelling for NO₂ can be successfully applied to informal peri-urban settlements in South Africa using similar predictor variables to those performed in Europe and North America. Explanatory power for PM_2.5 models is lower due to lower spatial variability and the possible impact of local transient sources. The study was able to provide NO₂ and PM_2.5 seasonal exposure estimates and maps for further health studies.

Analysis of remotely sensed and surface data of aerosols and meteorology for the Mexico Megalopolis Area between 2003 and 2015

This paper presents an aerosol characterization study from 2003 to 2015 for the Mexico City Metropolitan Area using remotely sensed aerosol data, ground-based measurements, air mass trajectory modeling, aerosol chemical composition modeling, and reanalysis data for the broader Megalopolis of Central Mexico region. The most extensive biomass burning emissions occur between March and May concurrent with the highest aerosol optical depth, ultraviolet aerosol index, and surface particulate matter (PM) mass concentration values. A notable enhancement in coarse PM levels is observed during vehicular rush hour periods on weekdays versus weekends owing to nonengine-related emissions such as resuspended dust. Among wet deposition species measured, PM_2.5, PM₁₀, and PM_coarse (PM₁₀-PM_2.5) were best correlated with NH₄⁺, SO₄^2-, and Ca²⁺, suggesting that the latter three constituents are important components of the aerosol seeding raindrops that eventually deposit to the surface in the study region. Reductions in surface PM mass concentrations were observed in 2014-2015 owing to reduced regional biomass burning as compared to 2003-2013.

Characteristics of columnar aerosol optical and microphysical properties retrieved from the sun photometer and its impact on radiative forcing over Skukuza (South Africa) during 1999-2010

The detailed analysis of columnar optical and microphysical properties of aerosols obtained from the AErosol RObotic NETwork (AERONET) Cimel sun photometer operated at Skukuza (24.98° S, 31.60° E, 150 m above sea level), South Africa was carried out using the level 2.0 direct sun and inversion products measured during 1999-2010. The observed aerosol optical depth (AOD) was generally low over the region, with high values noted in late winter (August) and mid-spring (September and October) seasons. The major aerosol types found during the study period were made of 3.74, 69.63, 9.34, 8.83, and 8.41% for polluted dust (PD), polluted continental (PC), non-absorbing (NA), slightly absorbing (SA), and moderately absorbing (MA) aerosols, respectively. Much attention was given to the aerosol fine- and coarse-modes deduced from the particle volume concentration, effective radius, and fine-mode volume fraction. The aerosol volume size distribution pattern was found to be bimodal with the fine-mode showing predominance relative to coarse-mode during the winter and spring seasons, owing to the onset of the biomass burning season. The mean values of total, fine-, and coarse-mode volume particle concentrations were 0.07 ± 0.04, 0.03 ± 0.03, and 0.04 ± 0.02 μm³ μm^-2, respectively, whereas the mean respective effective radii observed at Skukuza for the abovementioned modes were 0.35 ± 0.17, 0.14 ± 0.02, and 2.08 ± 0.02 μm. The averaged shortwave direct aerosol radiative forcing (ARF) observed within the atmosphere was found to be positive (absorption or heating effect), whereas the negative forcing in the surface and TOA depicted significant cooling effect due to more scattering type particles.

Temporal profile of PM10 and associated health effects in one of the most polluted cities of the world (Ahvaz, Iran) between 2009 and 2014

Ahvaz, Iran ranks as the most polluted city of the world in terms of PM₁₀ concentrations that lead to deleterious effects on its inhabitants. This study examines diurnal, weekly, monthly and annual fluctuations of PM₁₀ between 2009 and 2014 in Ahvaz. Health effects of PM₁₀ levels are also assessed using the World Health Organization AirQ software. Over the study period, the mean PM₁₀ level in Ahvaz was 249.5 µg m^-3, with maximum and minimum values in July (420.5 µg m^-3) and January (154.6 µg m^-3), respectively. The cumulative diurnal PM₁₀ profile exhibits a dominant peak between 08:00-11:00 (local time) with the lowest levels in the afternoon hours. While weekend PM₁₀ levels are not significantly reduced as compared to weekdays, an anthropogenic signature is instead observed diurnally on weekdays, which exhibit higher PM₁₀ levels between 07:00-17:00 by an average amount of 14.2 µg m^-3 as compared to weekend days. PM₁₀ has shown a steady mean-annual decline between 2009 (315.2 µg m^-3) and 2014 (143.5 µg m^-3). The AirQ model predicts that mortality was a health outcome for a total of 3777 individuals between 2009 and 2014 (i.e., 630 per year). The results of this study motivate more aggressive strategies in Ahvaz and similarly polluted desert cities to reduce the health effects of the enormous ambient aerosol concentrations.