A two-year assessment of particulate air pollution and sources in Kuwait

Evaluation of atmospheric particulate matter pollution characteristics in Shanghai based on biomagnetic monitoring technology

Atmospheric particulate matter (PM) pollution is one of the world's most serious environmental challenges, and it poses a significant threat to environmental quality and human health. Biomagnetic monitoring of PM has great potential to improve spatial resolution and provide alternative indicators for large area measurements, with respect and complementary to standard air quality monitoring stations. In this study, 160 samples of evergreen plant leaves were collected from park green spaces within five different functional areas of Shanghai. Magnetic properties were investigated to understand the extent and nature of particulate pollution and the possible sources, and to assess the suitability of various plant leaves for urban particulate pollution monitoring. The results showed that magnetic particles of the plant leaf-adherent PM were predominantly composed of pseudo-single domain (PSD) and multi-domain (MD) ferrimagnetic particles. Magnolia grandiflora, as a large evergreen arbor with robust PM retention capabilities, proved to be a more suitable candidate for monitoring urban particulate pollution compared to Osmanthus fragrans, a small evergreen arbor, and Aucuba japonica Thunb. var. variegata and Photinia serratifolia, evergreen shrubs. Meanwhile, there were significant differences in the spatial distribution of the magnetic particle content and heavy metal enrichment of the samples, mainly showing regional variations of industrial area > traffic area > commercial area > residential area > clean area. Additionally, the combination with the results of scanning electron microscopy, shows that industrial production (metal smelting, coal burning), transport and other activities are the main sources of particulate pollution. Plant leaves can be used as an effective tool for urban particulate pollution monitoring and assessment of atmospheric particulate pollution characteristics, and the technique provided useful information on particle size, mineralogy and possible sources.

Identifying mitigation strategies of comprehensive health centers against dust hazard: a qualitative study in Iran

BACKGROUND

Exposure to dust can disrupt healthcare services and severely affect all activity domains of the health system. The aim of this study was to explore mitigation strategies for comprehensive health centers against dust hazard.

METHOD

The present study was conducted using a qualitative design with a conventional content analysis approach in 2023. The participants in this study were managers and staff of comprehensive health centers and experts in health in disasters and emergencies in Kerman, Bam, Regan, and Ahvaz. Data were collected through interviews. Data collection continued until data saturation. The collected data were analyzed based on the steps proposed by Graneheim and Lundman. Participants' statements, after recording and transcribing, were categorized into semantic units. Data were analyzed by using MAXQDA software version 2020.

RESULTS

The analysis of the data with 23 participants revealed 106 Codes, 13 sub- categories and 5 main categories including: (A) reducing the impact of dust hazards, (B) management functions, (C) empowerment and performance improvement, (D) maintaining and promoting safety, and (E) Inter-sectoral coordination to implement mitigation strategies.

CONCLUSION

The findings showed that the mitigation strategies and solutions can be used by health policymakers and planners to reduce the impact of dust hazard, empower and motivate healthcare staff, develop training protocols to enhance risk perception of the staff and members of the community, create the necessary infrastructure for adoption of effective mitigation strategies in healthcare centers to create resilience and continue service delivery.

Identifying urban emission sources and their contribution to the oxidative potential of fine particulate matter (PM2.5) in Kuwait

In this study, we investigated the seasonal variations, chemical composition, sources, and oxidative potential of ambient PM2.5 (particles with a diameter of less than 2.5 μm) in Kuwait City. The sampling campaign was conducted within the premises of Kuwait Institute for Scientific Research from June 2022 to May 2023, covering different seasons throughout the year. The personal cascade impactor sampler (PCIS) operated at flow rate of 9 L/min was employed to collect weekly PM2.5 samples on PTFE and quarts filters. These collected samples were analyzed for carbonaceous species (i.e., elemental and organic carbon), metals and transition elements, inorganic ions, and DTT (dithiothreitol) redox activity. Furthermore, principal component analysis (PCA) and multi-linear regression (MLR) were used to identify the predominant emission sources and their percentage contribution to the redox activity of PM2.5 in Kuwait. The results of this study highlighted that the annual-averaged ambient PM2.5 mass concentrations in Kuwait (59.9 μg/m3) substantially exceeded the World Health Organization (WHO) guideline of 10 μg/m3. Additionally, the summer season displayed the highest PM2.5 mass concentration (75.2 μg/m3) compared to other seasons, primarily due to frequent dust events exacerbated by high-speed winds. The PCA identified four primary PM2.5 sources: mineral dust, fossil fuel combustion, road traffic, and secondary aerosols. The mineral dust was found to be the predominant source, contributing 36.1% to the PM2.5 mass, followed by fossil fuel combustion and traffic emissions with contributions of 23.7% and 20.3%, respectively. The findings of MLR revealed that road traffic was the most significant contributor to PM2.5 oxidative potential, accounting for 47% of the total DTT activity. In conclusion, this comprehensive investigation provides essential insights into the sources and health implications of PM2.5 in Kuwait, underscoring the critical need for effective air quality management strategies to mitigate the impacts of particulate pollution in the region.

Acute exposure to total and source-specific ambient fine particulate matter and risk of respiratory disease hospitalization in Kuwait

Many epidemiologic studies concerned with acute exposure to ambient PM2.5 have reported positive associations for respiratory disease hospitalization. However, few studies have investigated this relationship in Kuwait and extrapolating results from other regions may involve considerable uncertainty due to variations in concentration levels, particle sources and composition, and population characteristics. Local studies can provide evidence for strategies to reduce risks from episodic exposures to high levels of ambient PM2.5 and generating hypotheses for evaluating health risks from chronic exposures. Therefore, using speciated PM2.5 data from local samplers, we analyzed the impact of daily total and source-specific PM2.5 exposure on respiratory hospitalizations in Kuwait using a case-crossover design with conditional quasi-Poisson regression. Total and source-specific ambient PM2.5 were modeled using 0-5-day cumulative distributed lags. For total PM2.5, we observed a 0.16% (95% confidence interval [CI] = 0.05, 0.27%) increase in risk for respiratory hospitalization per 1 μg/m3 increase in concentration. Of the source factors assessed, dust demonstrated a statistically significant increase in risk (0.16%, 95% CI = 0.04, 0.29%), and the central estimate for regional PM2.5 was positive (0.11%) but not statistically significant (95% CI = -0.11, 0.33%). No effect was observed from traffic emissions and 'other' source factors. When hospitalizations were stratified by sex, nationality, and age, we found that female, Kuwaiti national, and adult groups had higher effect estimates. These results suggest that exposure to ambient PM2.5 is harmful in Kuwait and provide some evidence of differential toxicity and effect modification depending on the PM2.5 source and population affected.

Relationship between fine particulate matter (PM2.5) concentration and risk of hospitalization due to chronic obstructive pulmonary disease: a systematic review and meta-analysis

BACKGROUND

Short-term exposure to PM2.5 has been associated with human health risks. However, evidence on the association between short-term exposure to PM2.5 and the risk of chronic obstructive pulmonary disease (COPD) remains limited and controversial. This study aimed to specifically assess the relationship between exposure to PM2.5 and the risk of hospitalization due to COPD.

METHODS

A systematic search was conducted in PubMed, Web of Science, and Google Scholar databases from January 1, 2010 to May 1, 2022. The odds ratio (OR) statistic was calculated as a common measure of effect size. Publication bias was also examined in all eligible studies on COPD hospitalization using funnel plots and Egger's test, as well as trim-and-fill method for missing studies on COPD hospitalization.

RESULTS

A total of 19 studies were included in this meta-analysis. Random-effects models were plotted to calculate the pooled effect size by measuring OR (χ2 = 349.95; df = 18; I2 = 94.86%; P = 0.007; Z = 2.68; P < 0.001). A 10-mg/m3 daily increase in PM2.5 concentration was associated with a 1.6% (95% CI: 0.4-2.9%) increase in COPD hospitalization. There was no publication bias regarding the association between COPD hospitalization and PM2.5 (bias = 1.508; 95% CI: -1.475, 4.491; t = 1.066; P = 0.301). The subgroups of age ≥ 65 years and Asian countries were associated with an increased risk of COPD hospitalization. Besides, higher risks were estimated in the subgroups of studies performed in the warm season, case-crossover studies, studies with three lag days, and studies without adjustments for humidity and temperature confounders, with very small heterogeneity.

CONCLUSION

Evidence suggests that short-term exposure to PM2.5 increases COPD hospitalization. Further studies are needed to understand the mechanism of the association between PM2.5 and COPD for reducing air pollution, which can be beneficial for COPD patients.

Burden of fine air pollution on mortality in the desert climate of Kuwait

BACKGROUND

Middle Eastern desert countries like Kuwait are known for intense dust storms and enormous petrochemical industries affecting ambient air pollution. However, local health authorities have not been able to assess the health impacts of air pollution due to limited monitoring networks and a lack of historical exposure data.

OBJECTIVE

To assess the burden of PM_2.5 on mortality in the understudied dusty environment of Kuwait.

METHODS

We analyzed the acute impact of fine particulate matter (PM_2.5) on daily mortality in Kuwait between 2001 and 2016. To do so, we used spatiotemporally resolved estimates of PM_2.5 in the region. Our analysis explored factors such as cause of death, sex, age, and nationality. We fitted quasi-Poisson time-series regression for lagged PM_2.5 adjusted for time trend, seasonality, day of the week, temperature, and relative humidity.

RESULTS

There was a total of 70,321 deaths during the study period of 16 years. The average urban PM_2.5 was estimated to be 46.2 ± 19.8 µg/m³. A 10 µg/m³ increase in a 3-day moving average of urban PM_2.5 was associated with 1.19% (95% CI: 0.59, 1.80%) increase in all-cause mortality. For a 10 µg/m³ reduction in annual PM_2.5 concentrations, a total of 52.3 (95% CI: 25.7, 79.1) deaths each year could be averted in Kuwait. That is, 28.6 (95% CI: 10.3, 47.0) Kuwaitis, 23.9 (95% CI: 6.4, 41.5) non-Kuwaitis, 9.4 (95% CI: 1.2, 17.8) children, and 20.9 (95% CI: 4.3, 37.6) elderly deaths each year.

IMPACT STATEMENT

The overwhelming prevalence of devastating dust storms and enormous petrochemical industries in the Gulf and the Middle East has intensified the urgency to address air pollution and its detrimental health effects. Alarmingly, the region's epidemiological research lags behind, hindered by a paucity of ground monitoring networks and historical exposure data. In response, we are harnessing the power of big data to generate predictive models of air pollution across time and space, providing crucial insights into the mortality burden associated with air pollution in this under-researched yet critically impacted area.

Improved indoor air quality during desert dust storms: The impact of the MEDEA exposure-reduction strategies

Desert dust storms (DDS) are natural events that impact not only populations close to the emission sources but also populations many kilometers away. Countries located across the main dust sources, including countries in the Eastern Mediterranean, are highly affected by DDS. In addition, climate change is expanding arid areas exacerbating DDS events. Currently, there are no intervention measures with proven, quantified exposure reduction to desert dust particles. As part of the wider "MEDEA" project, co-funded by LIFE 2016 Programme, we examined the effectiveness of an indoor exposure-reduction intervention (i.e., decrease home ventilation during DDS events and continuous use of air purifier during DDS and non-DDS days) across homes and/or classrooms of schoolchildren with asthma and adults with atrial fibrillation in Cyprus and Crete-Greece. Participants were randomized to a control or intervention groups, including an indoor intervention group with exposure reduction measures and the use of air purifiers. Particle sampling, PM₁₀ and PM_2.5, was conducted in participants' homes and/or classrooms, between 2019 and 2022, during DDS-free weeks and during DDS days for as long as the event lasted. In indoor and outdoor PM₁₀ and PM_2.5 samples, mass and content in main and trace elements was determined. Indoor PM_2.5 and PM₁₀ mass concentrations, adjusting for premise type and dust conditions, were significantly lower in the indoor intervention group compared to the control group (PM_{2.5-intervention}/PM_2.5-control = 0.57, 95% CI: 0.47, 0.70; PM_{10-intervention}/PM_10-control = 0.59, 95% CI: 0.49, 0.71). In addition, the PM_2.5 and PM₁₀ particles of outdoor origin were significantly lower in the intervention vs. the control group (PM_2.5 infiltration intervention-to-control ratio: 0.49, 95% CI: 0.42, 0.58; PM₁₀ infiltration intervention-to-control ratio: 0.68, 95% CI: 0.52, 0.89). Our findings suggest that the use of air purifiers alongside decreased ventilation measures is an effective protective measure that reduces significantly indoor exposure to particles during DDS and non-DDS in high-risk population groups.

The effectiveness of traffic and production restrictions on urban air quality: A rare opportunity for investigation

Traffic and production restrictions are two important emergency measures for controlling urban air pollution. The lockdown policies implemented during the COVID-19 pandemic period are nearly equivalent to the policies of traffic and production restriction, which provides a rare opportunity to quantitatively evaluate the effectiveness of these emergency measures. Taking Wuhan, China as the study area, this paper firstly verified the changes in six air pollutants and analyzed their change rules in different lockdown periods using statistical methods. Then the structural breakpoints in air pollutants were detected via regression discontinuity design model. To comprehensively understand the effects of restrictions on air pollution, the influences of meteorological conditions on air pollution were also investigated. The results illustrated that the concentrations of PM_2.5, PM₁₀ and NO₂ decreased significantly during lockdown period. By comparing with the RDD coefficients of PM_2.5 (-34.46), PM₁₀ (-37.11) and NO₂ (-19.15), the lockdown had little effect on CO (-0.32). The traffic and production restrictions had no apparent effects on SO₂. Although O₃ showed an increasing trend, the increase was not limited to the lockdown period, meaning that the traffic and production restrictions had less effect on the increasing trend of O₃ concentration. Moreover, the structural breakpoints were verified in four air pollutants (PM_2.5, PM₁₀, NO₂, and CO), and the structural breakpoints were caused by lockdown instead of the Spring Festival. The results also indicated that the meteorological conditions were not the main reasons for the changes in air pollutants during the lockdown period. This paper reveals how the traffic and production restrictions affect urban air pollution and provides a strong implementation basis for the air pollution control policy.Implications: The traffic and production restrictions are two important emergency measures for controlling heavy urban air pollution. However, these two measures have never been implemented in a large area like a city for a long enough period, so the effectiveness of these two measures has never been estimated quantitatively at a city level. The lockdown policies implemented during the COVID-19 pandemic are nearly equivalent to the policies of traffic and production restriction, which provides a rare opportunity to quantitatively evaluate the effectiveness of these emergency measures. Thus, this study measured the effectiveness of production and traffic restrictions on different air pollutants. This study provides the following implications: (1) the dominant factors for air pollution changes during the lockdown are traffic and production restriction instead of meteorological conditions; (2) the production and traffic restriction policies are effective for reducing concentrations of PM_2.5, PM₁₀ and NO₂, while having less effect on O₃ and CO concentrations; (3) the sharp changes in air pollutants in 2020 are unlikely to be caused by the Spring Festival. These findings are crucial for making more comprehensive policies for protecting urban air quality.

Burden of cardiovascular disease attributable to particulate matter pollution in the eastern Mediterranean region: analysis of the 1990-2019 global burden of disease

AIMS

Particulate matter pollution is the most important environmental mediator of global cardiovascular morbidity and mortality. Air pollution evidence from the Eastern Mediterranean Region (EMR) is limited, owing to scarce local studies, and the omission from multinational studies. We sought to investigate trends of particulate matter (PM2.5)-related cardiovascular disease (CVD) burden in the EMR from 1990 to 2019.

METHODS AND RESULTS

We used the 1990-2019 global burden of disease methodology to investigate total PM2.5, ambient PM2.5, and household PM2.5-related CVD deaths and disability-adjusted life years (DALYs) and cause-specific CVD mortality in the EMR. The average annual population-weighted PM2.5 exposure in EMR region was 50.3 μg/m3 [95% confidence interval (CI):42.7-59.0] in 2019, which was comparable with 199 048.1 μg/m3 (95% CI: 36.5-65.3). This was despite an 80% reduction in household air pollution (HAP) sources since 1990. In 2019, particulate matter pollution contributed to 25.67% (95% CI: 23.55-27.90%) of total CVD deaths and 28.10% (95% CI: 25.75-30.37%) of DALYs in the region, most of which were due to ischaemic heart disease and stroke. We estimated that 353 071 (95% CI: 304 299-404 591) CVD deaths in EMR were attributable to particulate matter in 2019, including 264 877 (95% CI: 218 472-314 057) and 88 194.07 (95% CI: 60 149-119 949) CVD deaths from ambient PM2.5 pollution and HAP from solid fuels, respectively. DALY's in 2019 from CVD attributable to particulate matter was 28.1% when compared with 26.69% in 1990. The age-standardized death and DALY rates attributable to air pollution was 2122 per 100 000 in EMR in 2019 and was higher in males (2340 per 100 000) than in females (1882 per 100 000).

CONCLUSION

The EMR region experiences high PM2.5 levels with high regional heterogeneity and attributable burden of CVD due to air pollution. Despite significant reductions of overall HAP in the past 3 decades, there is continued HAP exposure in this region with rising trend in CVD mortality and DALYs attributable to ambient sources. Given the substantial contrast in disease burden, exposures, socio-economic and geo-political constraints in the EMR region, our analysis suggests substantial opportunities for PM2.5 attributable CVD burden mitigation.

Estimation of fine particulate matter in an arid area from visibility based on machine learning

BACKGROUND

The absence of air pollution monitoring networks makes it difficult to assess historical fine particulate matter (PM_2.5) exposures for countries in the areas, such as Kuwait, which are severe impacted by desert dust and anthropogenic pollution.

OBJECTIVE

We constructed an ensemble machine learning model to predict daily PM_2.5 concentrations for regions lack of PM_2.5 observations.

METHODS

The model was constructed based on daily PM_2.5, visibility, and other meteorological data collected at two sites in Kuwait. Then, our model was applied to predict the daily level of PM_2.5 concentrations for eight airports located in Kuwait and Iraq from 2013 to 2020.

RESULTS

As compared to traditional statistic models, the proposed machine learning methods improved the accuracy in using visibility to predict daily PM_2.5 concentrations with a cross-validation R² of 0.68. The predicted level of daily PM_2.5 concentrations were consistent with previous measurements. The predicted average yearly PM_2.5 concentration for the eight stations is 50.65 µg/m³. For all stations, the monthly average PM_2.5 concentrations reached their maximum in July and their minimum in November.

SIGNIFICANCE

These findings make it possible to retrospectively estimate daily PM_2.5 exposures using the large-scale databases of historical visibility in regions with few particulate matter monitoring stations.

IMPACT STATEMENT

The scarcity of air pollution ground monitoring networks makes it difficult to assess historical fine particulate matter exposures for countries in arid areas such as Kuwait. Visibility is closely related to atmospheric particulate matter concentrations and historical airport visibility records are commonly available in most countries. Our model make it possible to retrospectively estimate daily PM_2.5 exposures using the large-scale databases of historical visibility in arid regions with few particulate matter ground monitoring stations. The product of such models can be critical for environmental risk assessments and population health studies.

Source apportionment, identification and characterization, and emission inventory of ambient particulate matter in 22 Eastern Mediterranean Region countries: A systematic review and recommendations for good practice

Little is known about the main sources of ambient particulate matter (PM) in the 22 Eastern Mediterranean Region (EMR) countries. We designed this study to systematically review all published and unpublished source apportionment (SA), identification and characterization studies as well as emission inventories in the EMR. Of 440 articles identified, 82 (11 emission inventory ones) met our inclusion criteria for final analyses. Of 22 EMR countries, Iran with 30 articles had the highest number of studies on source specific PM followed by Pakistan (n = 15 articles) and Saudi Arabia (n = 8 papers). By contrast, there were no studies in Afghanistan, Bahrain, Djibouti, Libya, Somalia, Sudan, Syria, Tunisia, United Arab Emirates and Yemen. Approximately 72% of studies (51) were published within a span of 2015-2021.48 studies identified the sources of PM_2.5 and its constituents. Positive matrix factorization (PMF), principal component analysis (PCA) and chemical mass balance (CMB) were the most common approaches to identify the source contributions of ambient PM. Both secondary aerosols and dust, with 12-51% and 8-80% (33% and 30% for all EMR countries, on average) had the greatest contributions in ambient PM_2.5. The remaining sources for ambient PM_2.5, including mixed sources (traffic, industry and residential (TIR)), traffic, industries, biomass burning, and sea salt were in the range of approximately 4-69%, 4-49%, 1-53%, 7-25% and 3-29%, respectively. For PM₁₀, the most dominant source was dust with 7-95% (49% for all EMR countries, on average). The limited number of SA studies in the EMR countries (one study per approximately 9.6 million people) in comparison to Europe and North America (1 study per 4.3 and 2.1 million people respectively) can be augmented by future studies that will provide a better understanding of emission sources in the urban environment.

Mortality Attributable to Long-Term Exposure to Ambient Fine Particulate Matter: Insights from the Epidemiologic Evidence for Understudied Locations

Epidemiologic cohort studies have consistently demonstrated that long-term exposure to ambient fine particles (PM_2.5) is associated with mortality. Nevertheless, extrapolating results to understudied locations may involve considerable uncertainty. To explore this issue, this review discusses the evidence for (i) the associated risk of mortality, (ii) the shape of the concentration-response function, (iii) a causal interpretation, and (iv) how the source mix/composition of PM_2.5 and population characteristics may alter the effect. The accumulated evidence suggests the following: (i) In the United States, the change in all-cause mortality risk per μg/m³ is about 0.8%. (ii) The concentration-response function appears nonlinear. (iii) Causation is overwhelmingly supported. (iv) Fossil fuel combustion-related sources are likely more toxic than others, and age, race, and income may modify the effect. To illustrate the use of our findings in support of a risk assessment in an understudied setting, we consider Kuwait. However, given the complexity of this relationship and the heterogeneity in reported effects, it is unreasonable to think that, in such circumstances, point estimates can be meaningful. Consequently, quantitative probabilistic estimates, which cannot be derived objectively, become essential. Formally elicited expert judgment can provide such estimates, and this review provides the evidence to support an elicitation.

Air Pollution and Respiratory Hospital Admissions in Kuwait: The Epidemiological Applicability of Predicted PM2.5 in Arid Regions

Dust is a major component of fine particulate matter (PM_2.5) in arid regions; therefore, concentrations of this pollutant in countries such as Kuwait exceed air quality standards. There is limited understanding on the impact and burden of high PM_2.5 concentrations on morbidity in these countries. In this study, we explore the association of PM_2.5 and the risk of respiratory hospital admissions in Kuwait. A time-series regression model was used to investigate daily variations in respiratory admissions and PM_2.5 concentrations from 2010 to 2018. Due to the lack of historical air quality sampling in Kuwait, we used estimated daily PM_2.5 levels from a hybrid PM_2.5 prediction model. Individual and cumulative lag effects of PM_2.5 over a 5-day period were estimated using distributed lag linear models. Associations were stratified by sex, age, and nationality. There were 218,749 total respiratory admissions in Kuwait during the study period. Results indicate that for every 10 μg/m³ increase in PM_2.5, a 1.61% (95% CI = 0.87, 2.35%) increase in respiratory admissions followed over a 5-day cumulative lag. Our estimates show that a 10 μg/m³ reduction in average exposure will potentially avert 391 yearly respiratory admissions (95% CI = 211,571), with 265 fewer admissions among Kuwaitis (95% CI = 139,393) and 262 fewer admissions among children under 15 years of age (95% CI = 125,351). Different strata of the Kuwaiti population are vulnerable to respiratory hospitalization with short-term exposure to PM_2.5, especially those under 15 years of age. The findings are informative for public health authorities in Kuwait and other dust-prone countries.

Sampling Low Air Pollution Concentrations at a Neighborhood Scale in a Desert U.S. Metropolis with Volatile Weather Patterns

Background: Neighborhood-scale air pollution sampling methods have been used in a range of settings but not in low air pollution airsheds with extreme weather events such as volatile precipitation patterns and extreme summer heat and aridity—all of which will become increasingly common with climate change. The desert U.S. metropolis of Tucson, AZ, has historically low air pollution and a climate marked by volatile weather, presenting a unique opportunity. Methods: We adapted neighborhood-scale air pollution sampling methods to measure ambient NO₂, NO_x, and PM_2.5 and PM₁₀ in Tucson, AZ. Results: The air pollution concentrations in this location were well below regulatory guidelines and those of other locations using the same methods. While NO₂ and NO_x were reliably measured, PM_2.5 measurements were moderately correlated with those from a collocated reference monitor (r = 0.41, p = 0.13), potentially because of a combination of differences in inlet heights, oversampling of acutely high PM_2.5 events, and/or pump operation beyond temperature specifications. Conclusion: As the climate changes, sampling methods should be reevaluated for accuracy and precision, especially those that do not operate continuously. This is even more critical for low-pollution airsheds, as studies on low air pollution concentrations will help determine how such ambient exposures relate to health outcomes.

PM2.5 and PM10 during COVID-19 lockdown in Kuwait: Mixed effect of dust and meteorological covariates

This study investigated the impact of COVID-19 lockdown on particulate matter concentrations, specifically PM2.5 and PM10, in Kuwait. We studied the variations in PM2.5 and PM10 between the lockdown in 2020 with the corresponding periods of the years 2017-2019, and also investigated the differences in PM variations between the 'curfew' and 'non curfew' hours. We applied mixed-effect regression to investigate the factors that dictate PM variability (i.e., dust and meteorological covariates), and also processed satellite-based aerosol optical depths (AOD) to determine the spatial variability in aerosol loads. The results showed low PM2.5 concentration during the lockdown (33 μg/m3) compared to the corresponding previous three years (2017-2019); however, the PM10 concentration (122.5 μg/m3) increased relative to 2017 (116.6 μg/m3), and 2019 (92.8 μg/m3). After removing the 'dust effects', both PM2.5 and PM10 levels dropped by 18% and 31%, respectively. The mixed-effect regression model showed that high temperature and high wind speed were the main contributors to high PM2.5 and PM10, respectively, in addition to the dust haze and blowing dust. This study highlights that the reductions of anthropogenic source emissions are overwhelmed by dust events and adverse meteorology in arid regions, and that the lockdown did not reduce the high concentrations of PM in Kuwait.

Temporal and Spatial Autocorrelation as Determinants of Regional AOD-PM2.5 Model Performance in the Middle East

Exposure to fine particulate matter (PM2.5) air pollution has been shown in numerous studies to be associated with detrimental health effects. However, the ability to conduct epidemiological assessments can be limited due to challenges in generating reliable PM2.5 estimates, particularly in parts of the world such as the Middle East where measurements are scarce and extreme meteorological events such as sandstorms are frequent. In order to supplement exposure modeling efforts under such conditions, satellite-retrieved aerosol optical depth (AOD) has proven to be useful due to its global coverage. By using AODs from the Multiangle Implementation of Atmospheric Correction (MAIAC) of the MODerate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) combined with meteorological and assimilated aerosol information from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), we constructed machine learning models to predict PM2.5 in the area surrounding the Persian Gulf, including Kuwait, Bahrain, and the United Arab Emirates (U.A.E). Our models showed regional differences in predictive performance, with better results in the U.A.E. (median test R2 = 0.66) than Kuwait (median test R2 = 0.51). Variable importance also differed by region, where satellite-retrieved AOD variables were more important for predicting PM2.5 in Kuwait than in the U.A.E. Divergent trends in the temporal and spatial autocorrelations of PM2.5 and AOD in the two regions offered possible explanations for differences in predictive performance and variable importance. In a test of model transferability, we found that models trained in one region and applied to another did not predict PM2.5 well, even if the transferred model had better performance. Overall the results of our study suggest that models developed over large geographic areas could generate PM2.5 estimates with greater uncertainty than could be obtained by taking a regional modeling approach. Furthermore, development of methods to better incorporate spatial and temporal autocorrelations in machine learning models warrants further examination.