Harbor and intra-city drivers of air pollution: findings from a land use regression model, Durban, South Africa

Multiple land use regression models (LUR) were developed for different air pollutants to characterize exposure, in the Durban metropolitan area, South Africa. Based on the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology, concentrations of particulate matter (PM; 10; and PM; 2.5; ), sulphur dioxide (SO; 2; ), and nitrogen dioxide (NO; 2; ) were measured over a 1-year period, at 41 sites, with Ogawa Badges and 21 sites with PM Monitors. Sampling was undertaken in two regions of the city of Durban, South Africa, one with high levels of heavy industry as well as a harbor, and the other small-scale business activity. Air pollution concentrations showed a clear seasonal trend with higher concentrations being measured during winter (25.8, 4.2, 50.4, and 20.9 µg/m; 3; for NO; 2; , SO; 2; , PM; 10; , and PM; 2.5; , respectively) as compared to summer (10.5, 2.8, 20.5, and 8.5 µg/m; 3; for NO; 2; , SO; 2; , PM; 10; , and PM; 2.5; , respectively). Furthermore, higher levels of NO; 2; and SO; 2; were measured in south Durban as compared to north Durban as these are industrial related pollutants, while higher levels of PM were measured in north Durban as compared to south Durban and can be attributed to either traffic or domestic fuel burning. The LUR NO; 2; models for annual, summer, and winter explained 56%, 41%, and 63% of the variance with elevation, traffic, population, and Harbor being identified as important predictors. The SO; 2; models were less robust with lower R; 2; annual (37%), summer (46%), and winter (46%) with industrial and traffic variables being important predictors. The R; 2; for PM; 10; models ranged from 52% to 80% while for PM; 2.5; models this range was 61-76% with traffic, elevation, population, and urban land use type emerging as predictor variables. While these results demonstrate the influence of industrial and traffic emissions on air pollution concentrations, our study highlighted the importance of a Harbor variable, which may serve as a proxy for NO; 2; concentrations suggesting the presence of not only ship emissions, but also other sources such as heavy duty motor vehicles associated with the port activities

Data integration for high-resolution, continental-scale estimation of air pollution concentrations

Air pollution constitutes the highest environmental risk factor in relation to heath. In order to provide the evidence required for health impact analyses, to inform policy and to develop potential mitigation strategies comprehensive information is required on the state of air pollution. Information on air pollution traditionally comes from ground monitoring (GM) networks but these may not be able to provide sufficient coverage and may need to be supplemented with information from other sources (e.g. chemical transport models; CTMs). However, these may only be available on grids and may not capture micro-scale features that may be important in assessing air quality in areas of high population. We develop a model that allows calibration between multiple data sources available at different levels of support by allowing the coefficients of calibration equations to vary over space and time, enabling downscaling where the data is sufficient to support it. The model is used to produce high-resolution (1km $\times$ 1km) estimates of NO$_2$ and PM$_{2.5}$ across Western Europe for 2010-2016. Concentrations of both pollutants are decreasing during this period, however there remain large populations exposed to levels exceeding the WHO Air Quality Guidelines and thus air pollution remains a serious threat to health

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

Childhood cancer and traffic-related air pollution in Switzerland: A nationwide census-based cohort study.

Motor vehicle exhaust is a major contributor to air pollution, and exposure to benzene or other carcinogenic components may increase cancer risks. We aimed to investigate the association between traffic-related air pollution and risk of childhood cancer in a nationwide cohort study in Switzerland. We identified incident cases from the Swiss Childhood Cancer Registry diagnosed < 16 years of age between 1990 and 2015 and linked them probabilistically with the census-based Swiss National Cohort study. We developed land use regression models to estimate annual mean ambient levels of nitrogen dioxide (NO2) and benzene outside 1.4 million children's homes. We used risk-set sampling to facilitate the analysis of time-varying exposure and fitted conditional logistic regression models adjusting for neighborhood socio-economic position, level of urbanization, and background ionizing radiation. We included 2,960 cancer cases in the analyses. The adjusted hazard ratios (HR) and 95% confidence intervals for exposure to NO2 per 10 μg/m3 were 1.00 (95%-CI 0.88-1.13) for acute lymphoblastic leukemia (ALL) and 1.31 (95%-CI 1.00-1.71) for acute myeloid leukemia (AML). Using exposure lagged by 1 to 5 years instead of current exposure attenuated the effect for AML. The adjusted HR for exposure to benzene per 1 μg/m3 was 1.03 (95%-CI 0.86-1.23) for ALL and 1.29 (95%-CI 0.86-1.95) for AML. We also observed increased HRs for other diagnostic groups, notably non-Hodgkin lymphoma. Our study adds to the existing evidence that exposure to traffic-related air pollution is associated with an increased risk of childhood leukemia, particularly AML

NO2 and PM2.5 Exposures and Lung Function in Swiss Adults: Estimated Effects of Short-Term Exposures and Long-Term Exposures with and without Adjustment for Short-Term Deviations

Background: The impact of nitrogen dioxide (NO2) and particulate matter with an aerodynamic diameter of less than or equal to 2.5. microns (PM2.5) exposures on lung function has been investigated mainly in children and less in adults. Furthermore, it is unclear whether short-term deviations of air pollutant concentration need to be considered in long-term exposure models. Objectives: The aims of this study were to investigate the association between short-term air pollution exposure and lung function and to assess whether short-term deviations of air pollutant concentration should be integrated into long-term exposure models. Methods: Short-term (daily averages 0–7 d prior) and long-term (1- and 4-y means) NO2 and PM2.5 concentrations were modeled using satellite, land use, and meteorological data calibrated on ground measurements. Forced expiratory volume within the first second (FEV1) of forced exhalation and forced vital capacity (FVC) were measured during a LuftiBus assessment (2003–2012) and linked to exposure information from the Swiss National Cohort for 36,085 adults (ages 18–95 y). We used multiple linear regression to estimate adjusted associations, and additionally adjusted models of long-term exposures for short-term deviations in air pollutant concentrations. Results: A 10μg/m3 increase in NO2 and PM2.5 on the day of the pulmonary function test was associated with lower FEV1 and FVC (NO2: FEV1 −8.0 ml [95% confidence interval: −13.4, −2.7], FVC −16.7 ml [−23.4, −10.0]; PM2.5: FEV1 −15.3 ml [−21.9, −8.7], FVC −18.5 ml [−26.5, −10.5]). A 10μg/m3 increase in 1-y mean NO2 was also associated with lower FEV1 (−7.7 ml; −15.9, 0.5) and FVC (−21.6 ml; −31.9, −11.4), as was a 10μg/m3 increase in 1-y mean PM2.5 (FEV1: −42.2 ml; −56.9, −27.5; FVC: −82.0 ml; −100.1, −63.9). These associations were robust to adjustment for short-term deviations in the concentration of each air pollutant. Conclusions: Short- and long-term air pollution exposures were negatively associated with lung function, in particular long-term PM2.5 exposure with FVC. Our findings contribute substantially to the evidence of adverse associations between air pollution and lung function in adults. https://doi.org/10.1289/EHP752

Ambient Air Pollution and Cardiorespiratory Outcomes amongst Adults Residing in Four Informal Settlements in the Western Province of South Africa

Few studies have investigated the relationship between ambient air pollution and cardiorespiratory outcomes in Africa. A cross-sectional study comprising of 572 adults from four informal settlements in the Western Cape, South Africa was conducted. Participants completed a questionnaire adapted from the European Community Respiratory Health Survey, and the National Health and Nutrition Examination Survey questionnaire. Exposure estimates were previously modelled using Land-Use Regression for Particulate Matter (PM2.5) and Nitrogen Dioxide (NO2) at participants&rsquo; homes. The median age of the participants was 40.7 years, and 88.5% were female. The median annual NO2 level was 19.7 &micro;g/m3 (interquartile range [IQR: 9.6&ndash;23.7]) and the median annual PM2.5 level was 9.7 &micro;g/m3 (IQR: 7.3&ndash;12.4). Logistic regression analysis was used to assess associations between outcome variables and air pollutants. An interquartile range increase of 5.12 &micro;g/m3 in PM2.5 was significantly associated with an increased prevalence of self-reported chest-pain, [Odds ratio: 1.38 (95% CI: 1.06&ndash;1.80)], adjusting for NO2, and other covariates. The study found preliminary circumstantial evidence of an association between annual ambient PM2.5 exposure and self-reported chest-pain (a crude proxy of angina-related pain), even at levels below the South African National Ambient Air Quality Standards

Integrating large-scale stationary and local mobile measurements to estimate hyperlocal long-term air pollution using transfer learning methods

Mobile air quality measurements are collected typically for several seconds per road segment and in specific timeslots (e.g., working hours). These short-term and on-road characteristics of mobile measurements become the ubiquitous shortcomings of applying land use regression (LUR) models to estimate long-term concentrations at residential addresses. This issue was previously found to be mitigated by transferring LUR models to the long-term residential domain using routine long-term measurements in the studied region as the transfer target (local scale). However, long-term measurements are generally sparse in individual cities. For this scenario, we propose an alternative by taking long-term measurements collected over a larger geographical area (global scale) as the transfer target and local mobile measurements as the source (Global2Local model). We empirically tested national, airshed countries (i.e., national plus neighboring countries) and Europe as the global scale in developing Global2Local models to map nitrogen dioxide (NO 2) concentrations in Amsterdam. The airshed countries scale provided the lowest absolute errors, and the Europe-wide scale had the highest R 2. Compared to a "global" LUR model (trained exclusively with European-wide long-term measurements), and a local mobile LUR model (using mobile data from Amsterdam only), the Global2Local model significantly reduced the absolute error of the local mobile LUR model (root-mean-square error, 6.9 vs 12.6 μg/m 3) and improved the percentage explained variances compared to the global model (R 2, 0.43 vs 0.28, assessed by independent long-term NO 2 measurements in Amsterdam, n = 90). The Global2Local method improves the generalizability of mobile measurements in mapping long-term residential concentrations with a fine spatial resolution, which is preferred in environmental epidemiological studies

Air pollution exposure during pregnancy and lung function in childhood: The LUIS study.

BACKGROUND The adverse effects of high air pollution levels on childhood lung function are well-known. Limited evidence exists on the effects of moderate exposure levels during early life on childhood lung function. We investigated the association of exposure to moderate air pollution during pregnancy, infancy, and preschool time with lung function at school age in a Swiss population-based study. METHODS Fine-scale spatiotemporal model estimates of particulate matter with a diameter <2.5 µm (PM2.5) and nitrogen dioxide (NO2) were linked with residential address histories. We compared air pollution exposures within different time windows (whole pregnancy, first, second, and third trimester of pregnancy, first year of life, preschool age) with forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) measured cross-sectionally using linear regression models adjusted for potential confounders. RESULTS We included 2182 children, ages 6-17 years. Prenatal air pollution exposure was associated with reduced lung function at school age. In children aged 12 years, per 10 µg·m-3 increase in PM2.5 during pregnancy, FEV1 was 55 mL lower (95% CI -84 to -25 mL) and FVC 62 mL lower (95% CI -96 to -28 mL). Associations were age-dependent since they were stronger in younger and weaker in older children. PM2.5 exposure after birth was not associated with reduced lung function. There was no association between NO2 exposure and lung function. CONCLUSION In utero lung development is most sensitive to air pollution exposure, since even modest PM2.5 exposure during the prenatal time was associated with reduced lung function, most prominent in younger children

Modelling Europe-wide fine resolution daily ambient temperature for 2003–2020 using machine learning

To improve our understanding of the health impacts of high and low temperatures, epidemiological studies require spatiotemporally resolved ambient temperature (Ta) surfaces. Exposure assessment over various European cities for multi-cohort studies requires high resolution and harmonized exposures over larger spatiotemporal extents. Our aim was to develop daily mean, minimum and maximum ambient temperature surfaces with a 1 × 1 km resolution for Europe for the 2003–2020 period. We used a two-stage random forest modelling approach. Random forest was used to (1) impute missing satellite derived Land Surface Temperature (LST) using vegetation and weather variables and to (2) use the gap-filled LST together with land use and meteorological variables to model spatial and temporal variation in Ta measured at weather stations. To assess performance, we validated these models using random and block validation. In addition to global performance, and to assess model stability, we reported model performance at a higher granularity (local). Globally, our models explained on average more than 81 % and 93 % of the variability in the block validation sets for LST and Ta respectively. Average RMSE was 1.3, 1.9 and 1.7 °C for mean, min and max ambient temperature respectively, indicating a generally good performance. For Ta models, local performance was stable across most of the spatiotemporal extent, but showed lower performance in areas with low observation density. Overall, model stability and performance were lower when using block validation compared to random validation. The presented models will facilitate harmonized high-resolution exposure assignment for multi-cohort studies at a European scale

Spatial variability of nitrogen dioxide and formaldehyde and residential exposure of children in the industrial area of Viadana, Northern Italy

Chipboard production is a source of ambient air pollution. We assessed the spatial variability of outdoor pollutants and residential exposure of children living in proximity to the largest chipboard industry in Italy, and evaluated the reliability of exposure estimates obtained from a number of available models. We obtained passive sampling data on NO2 and formaldehyde collected by the environmental protection agency of Lombardia region at 25 sites in the municipality of Viadana during 10 weeks (2017-18), and compared NO2 measurements with average weekly concentrations from continuous monitors. We compared interpolated NO2 and formaldehyde surfaces with previous maps for 2010. We assessed the relationship between residential proximity to the industry and pollutant exposures assigned using these maps, as well as other available countrywide/continental models based on routine data on NO2, PM10, and PM2.5. The correlation between NO2 concentrations from continuous and passive sampling was high (Pearson\u2019s r=0.89), although passive sampling underestimated NO2 especially during winter. For both 2010 and 2017-18, we observed higher NO2 and formaldehyde concentrations in the south of Viadana, with hot-spots in proximity to the industry. PM10 and PM2.5 exposures were higher for children at &lt;1km compared to the children living at &gt;3.5 km to the industry, whereas NO2 exposure was higher at 1-1.7 km to the industry. Road and population densities were also higher close to the industry. Findings from a variety of exposure models suggest that children living in proximity to the chipboard industry in Viadana are more exposed to air pollution, and that exposure gradients are relatively stable over time