Effects of Population Weighting on PM10 Concentration Estimation

A spatial approach to assessing PM2.5 exposure level of a brickmaking community in South Africa

Globally, particulate matter with an aerodynamic diameter of 2.5 µm or less poses a significant threat to human health. The first step in quantifying human health impacts caused by exposure to PM2.5 pollution is exposure assessment. Population-weighted exposure level (PWEL) estimation is one of the methods that provides a more precise exposure assessment since it incorporates the spatiotemporal distribution of population with the pollution concentration estimate. In this study, PM2.5 exposure levels in the local communities around brickmaking industries were investigated, using the population census data of the study area and 1-year data from nine PM2.5 monitoring stations installed in and around the brickmaking industries. The observed PM2.5 data was spatially interpolated using inverse distance weight (IDW). Data on PM2.5 levels across the study area were classified based on the World Health Organization interim target (IT) guidelines and the South African National ambient air quality standard (NAAQS). An annual PM2.5 population weighted exposure level of 27.6 µg/m3 was estimated for the study area. However, seasonal exposure levels of 28.9, 37.6, 26.5, and 20.7 µg/m3 were estimated for the autumn, winter, spring, and summer seasons, respectively. This implies that local communities around the brick kiln in the Vhembe District are exposed to high levels of PM2.5, especially in winter. The PM2.5 levels in the brickmaking industries as well as its other sources in the Vhembe District, therefore, need to be lowered. Findings from population exposure level to pollutants can provide valuable data for formulating policies and recommendations on exposure reduction and public health protection.Implications: PM2.5 concentration in any given environment has high spatial and temporal variability due to the presence of diffused sources in the environment. Using ambient air concentrations to directly estimate population exposure without taking into consideration the disproportionate spatial and temporal distribution of the pollutant and the population may not yield accurate results on human exposure levels. It is, therefore, important to assess the aggregated PM2.5 exposure of a populace within a given area. This study therefore examines the PM2.5 population-weighted-exposure level of the host communities of the brickmaking industry in Vhembe District, South Africa.

Cardiovascular, respiratory and all-cause (natural) health endpoint estimation using a spatial approach in Malaysia

In 2016, the World Health Organization (WHO) estimated that approximately 4.2 million premature deaths worldwide were attributable to exposure to particulate matter 2.5 μm (PM_2.5). This study assessed the environmental burden of disease attributable to PM_2.5 at the national level in Malaysia. We estimated the population-weighted exposure level (PWEL) of PM10 concentrations in Malaysia for 2000, 2008, and 2013 using aerosol optical density (AOD) data from publicly available remote sensing satellite data (MODIS Terra). The PWEL was then converted to PM_2.5 using Malaysia's WHO ambient air conversion factor. We used AirQ+ 2.0 software to calculate all-cause (natural), ischemic heart disease (IHD), stroke, chronic obstructive pulmonary disease (COPD), lung cancer (LC), and acute lower respiratory infection (ALRI) excess deaths from the National Burden of Disease data for 2000, 2008 and 2013. The average PWELs for annual PM_2.5 for 2000, 2008, and 2013 were 22 μg m-3, 18 μg m-3 and 24 μg m-3, respectively. Using the WHO 2005 Air Quality Guideline cut-off point of PM_2.5 of 10 μg m-3, the estimated excess deaths for 2000, 2008, and 2013 from all-cause (natural) mortality were between 5893 and 9781 (95 % CI: 3347-12,791), COPD was between 164 and 957 (95 % CI: 95-1411), lung cancer was between 109 and 307 (95 % CI: 63-437), IHD was between 3 and 163 deaths, according to age groups (95 % CI: 2-394) and stroke was between 6 and 155 deaths, according to age groups (95 % CI: 3-261). An increase in estimated health endpoints was associated with increased estimated PWEL PM_2.5 for 2013 compared to 2000 and 2008. Adhering the ambient PM_2.5 level to the Malaysian Air Quality Standard IT-2 would reduce the national health endpoints mortality.

COVID-19 infection rate but not severity is associated with availability of greenness in the United States

Human exposure to greenness is associated with COVID-19 prevalence and severity, but most relevant research has focused on the relationships between greenness and COVID-19 infection rates. In contrast, relatively little is known about the associations between greenness and COVID-19 hospitalizations and deaths, which are important for risk assessment, resource allocation, and intervention strategies. Moreover, it is unclear whether greenness could help reduce health inequities by offering more benefits to disadvantaged populations. Here, we estimated the associations between availability of greenness (expressed as population-density-weighted normalized difference vegetation index) and COVID-19 outcomes across the urban-rural continuum gradient in the United States using generalized additive models with a negative binomial distribution. We aggregated individual COVID-19 records at the county level, which includes 3,040 counties for COVID-19 case infection rates, 1,397 counties for case hospitalization rates, and 1,305 counties for case fatality rates. Our area-level ecological study suggests that although availability of greenness shows null relationships with COVID-19 case hospitalization and fatality rates, COVID-19 infection rate is statistically significant and negatively associated with more greenness availability. When performing stratified analyses by different sociodemographic groups, availability of greenness shows stronger negative associations for men than for women, and for adults than for the elderly. This indicates that greenness might have greater health benefits for the former than the latter, and thus has limited effects for ameliorating COVID-19 related inequity. The revealed greenness-COVID-19 links across different space, time and sociodemographic groups provide working hypotheses for the targeted design of nature-based interventions and greening policies to benefit human well-being and reduce health inequity. This has important implications for the post-pandemic recovery and future public health crises.

Estimating the burden of disease attributable to ambient air pollution (ambient PM2.5 and ambient ozone) in South Africa for 2000, 2006 and 2012

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Importance of Punctual Monitoring to Evaluate the Health Effects of Airborne Particulate Matter

Particulate matter (PM) pollution is one of the major public health problems worldwide, given the high mortality attributable to exposure to PM pollution and the high pathogenicity that is found above all in the respiratory, cardiovascular, and neurological systems. The main sources of PM pollution are the daily use of fuels (wood, coal, organic residues) in appliances without emissions abatement systems, industrial emissions, and vehicular traffic. This review aims to investigate the causes of PM pollution and classify the different types of dust based on their size. The health effects of exposure to PM will also be discussed. Particular attention is paid to the measurement method, which is unsuitable in the risk assessment process, as the evaluation of the average PM compared to the evaluation of PM with punctual monitoring significantly underestimates the health risk induced by the achievement of high PM values, even for limited periods of time.

Development and Validation of a Sub-National, Satellite-Based Land-Use Regression Model for Annual Nitrogen Dioxide Concentrations in North-Western China

Existing national- or continental-scale models of nitrogen dioxide (NO₂) exposure have a limited capacity to capture subnational spatial variability in sparsely-populated parts of the world where NO₂ sources may vary. To test and validate our approach, we developed a land-use regression (LUR) model for NO₂ for Ningxia Hui Autonomous Region (NHAR) and surrounding areas, a small rural province in north-western China. Using hourly NO₂ measurements from 105 continuous monitoring sites in 2019, a supervised, forward addition, linear regression approach was adopted to develop the model, assessing 270 potential predictor variables, including tropospheric NO₂, optically measured by the Aura satellite. The final model was cross-validated (5-fold cross validation), and its historical performance (back to 2014) assessed using 41 independent monitoring sites not used for model development. The final model captured 63% of annual NO₂ in NHAR (RMSE: 6 ppb (21% of the mean of all monitoring sites)) and contiguous parts of Inner Mongolia, Gansu, and Shaanxi Provinces. Cross-validation and independent evaluation against historical data yielded adjusted R² values that were 1% and 10% lower than the model development values, respectively, with comparable RMSE. The findings suggest that a parsimonious, satellite-based LUR model is robust and can be used to capture spatial contrasts in annual NO₂ in the relatively sparsely-populated areas in NHAR and neighbouring provinces.