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Air pollution is one of the leading causes of mortality worldwide. An accurate assessment of its spatial and temporal distribution is mandatory to conduct epidemiological studies able to estimate long-term (e.g., annual) and short-term (e.g., daily) health effects. While spatiotemporal models for particulate matter (PM) have been developed in several countries, estimates of daily nitrogen dioxide (NO2) and ozone (O3) concentrations at high spatial resolution are lacking, and no such models have been developed in Sweden. We collected data on daily air pollutant concentrations from routine monitoring networks over the period 2005–2016 and matched them with satellite data, dispersion models, meteorological parameters, and land-use variables. We developed a machine-learning approach, the random forest (RF), to estimate daily concentrations of PM10 (PM<10 microns), PM2.5 (PM<2.5 microns), PM2.5–10 (PM between 2.5 and 10 microns), NO2, and O3 for each squared kilometer of Sweden over the period 2005–2016. Our models were able to describe between 64% (PM10) and 78% (O3) of air pollutant variability in held-out observations, and between 37% (NO2) and 61% (O3) in held-out monitors, with no major differences across years and seasons and better performance in larger cities such as Stockholm. These estimates will allow to investigate air pollution effects across the whole of Sweden, including suburban and rural areas, previously neglected by epidemiological investigations.

Background Epidemiological studies of long-term exposure to outdoor air pollution have consistently documented associations with morbidity and mortality. Air pollution exposure in these epidemiological studies is generally assessed at the residential address, because individual time-activity patterns are seldom known in large epidemiological studies. Ignoring time-activity patterns may result in bias in epidemiological studies. The aims of this paper are to assess the agreement between exposure assessed at the residential address and exposures estimated with time-activity integrated and the potential bias in epidemiological studies when exposure is estimated at the residential address. Main body We reviewed exposure studies that have compared residential and time-activity integrated exposures, with a focus on the correlation. We further discuss epidemiological studies that have compared health effect estimates between the residential and time-activity integrated exposure and studies that have indirectly estimated the potential bias in health effect estimates in epidemiological studies related to ignoring time-activity patterns. A large number of studies compared residential and time-activity integrated exposure, especially in Europe and North America, mostly focusing on differences in level. Eleven of these studies reported correlations, showing that the correlation between residential address-based and time-activity integrated long-term air pollution exposure was generally high to very high ( R  > 0.8). For individual subjects large differences were found between residential and time-activity integrated exposures. Consistent with the high correlation, five of six identified epidemiological studies found nearly identical health effects using residential and time-activity integrated exposure. Six additional studies in Europe and North America showed only small to moderate potential bias (9 to 30% potential underestimation) in estimated exposure response functions using residence-based exposures. Differences of average exposure level were generally small and in both directions. Exposure contrasts were smaller for time-activity integrated exposures in nearly all studies. The difference in exposure was not equally distributed across the population including between different socio-economic groups. Conclusions Overall, the bias in epidemiological studies related to assessing long-term exposure at the residential address only is likely small in populations comparable to those evaluated in the comparison studies. Further improvements in exposure assessment especially for large populations remain useful.

Heat-related morbidity and mortality are increasing due to climate change, emphasizing the need to identify vulnerable areas and people exposed to extreme temperatures. To improve heat stress impact assessment, we developed a replicable machine learning model that integrates remote sensing, ground station, and geospatial data to estimate daily air temperature at a spatial resolution of 100 m × 100 m across the region of Tuscany, Italy. Using a two-stage approach, we first imputed missing land surface temperature data from MODIS using gradient-boosted trees and spatio-temporal predictors. Then, we modeled daily maximum and minimum air temperatures by incorporating monitoring station observations, satellite-derived data (MODIS, Landsat 8), topography, land cover, meteorological variables (ERA5-land), and vegetation indices (NDVI). The model achieved high predictive accuracy, with R2 values of 0.95 for Tmax and 0.92 for Tmin, and root mean square errors (RMSE) of 1.95 °C and 1.96 °C, respectively. It effectively captured both temporal (R2: 0.95; 0.94) and spatial (R2: 0.92; 0.72) temperature variations, allowing for the creation of high-resolution maps. These results highlight the potential of integrating Earth Observation and machine learning to generate high-resolution temperature maps, offering valuable insights for urban planning, climate adaptation, and epidemiological studies on heat-related health effects.

Background Policies on waste disposal in Europe are heterogeneous and rapidly changing, with potential health implications that are largely unknown. We conducted a health impact assessment of landfilling and incineration in three European countries: Italy, Slovakia and England. Methods A total of 49 (Italy), 2 (Slovakia), and 11 (England) incinerators were operating in 2001 while for landfills the figures were 619, 121 and 232, respectively. The study population consisted of residents living within 3 km of an incinerator and 2 km of a landfill. Excess risk estimates from epidemiological studies were used, combined with air pollution dispersion modelling for particulate matter (PM 10 ) and nitrogen dioxide (NO 2 ). For incinerators, we estimated attributable cancer incidence and years of life lost (YoLL), while for landfills we estimated attributable cases of congenital anomalies and low birth weight infants. Results About 1,000,000, 16,000, and 1,200,000 subjects lived close to incinerators in Italy, Slovakia and England, respectively. The additional contribution to NO 2 levels within a 3 km radius was 0.23, 0.15, and 0.14 μg/m 3 , respectively. Lower values were found for PM 10 . Assuming that the incinerators continue to operate until 2020, we are moderately confident that the annual number of cancer cases due to exposure in 2001-2020 will reach 11, 0, and 7 in 2020 and then decline to 0 in the three countries in 2050. We are moderately confident that by 2050, the attributable impact on the 2001 cohort of residents will be 3,621 (Italy), 37 (Slovakia) and 3,966 (England) YoLL. The total exposed population to landfills was 1,350,000, 329,000, and 1,425,000 subjects, respectively. We are moderately confident that the annual additional cases of congenital anomalies up to 2030 will be approximately 2, 2, and 3 whereas there will be 42, 13, and 59 additional low-birth weight newborns, respectively. Conclusions The current health impacts of landfilling and incineration can be characterized as moderate when compared to other sources of environmental pollution, e.g. traffic or industrial emissions, that have an impact on public health. There are several uncertainties and critical assumptions in the assessment model, but it provides insight into the relative health impact attributable to waste management.

Air pollution is associated with health in childhood. However, there is limited evidence on sensitive periods during the first 18 years of life. Data were drawn from the Millennium Cohort Study, a large and nationally representative cohort born in 2000/2002. Self-reported general health was assessed at age 17; number of hospital records were derived from linked health data (Hospital Episode Statistics) for consented participants. Residential history was linked to 25 × 25 m grid resolution annual PM 2.5 , PM 10 and NO 2 maps between 2000 and 2019; year-specific air pollution exposure in 200-m buffers around postcode centroids were computed. After adjusting for individual and time-variant area-level confounders, children exposed to higher air pollution in early (2–4 y) ( n  = 9137; PM 2.5 : OR = 1.06, 95% CI: 1.01–1.11; PM 10 : OR = 1.05, 95% CI: 1.01–1.09; NO 2 : OR = 1.01, 95% CI: 1.00–1.02) and middle childhood (5–7) ( n  = 9171; PM 2.5 : OR = 1.04, 95% CI: 1.00–1.07; PM 10 : OR = 1.03, 95% CI: 1.01–1.06) reported worse general health at age 17. Higher PM 2.5 and NO 2 exposure in adolescence increased the number of hospital episodes in young adulthood. Individuals from non-White and disadvantaged backgrounds were exposed to higher levels of air pollution. Air pollution in early and middle childhood might contribute to worse general health, with ethnic minority and disadvantaged children being more exposed.

Black carbon (BC), a component of fine particulate matter [particles with an aerodynamic diameter ( )], may contribute to carcinogenic effects of air pollution. Until recently however, there has been little evidence to evaluate this hypothesis. This study aimed to estimate the associations between long-term exposure to BC and risk of cancer. This study was conducted within the French Gazel cohort of 20,625 subjects. We assessed exposure to BC by linking subjects' histories of residential addresses to a map of European black carbon levels in 2010 with back- and forward-extrapolation between 1989 and 2015. We used extended Cox models, with attained age as time-scale and time-varying cumulative exposure to BC, adjusted for relevant sociodemographic and lifestyle variables. To consider latency between exposure and cancer diagnosis, we implemented a 10-y lag, and as a sensitivity analysis, a lag of 2 y. To isolate the effect of BC from that of total , we regressed BC on and used the residuals as the exposure variable. During the 26-y follow-up period, there were 3,711 incident cancer cases (all sites combined) and 349 incident lung cancers. Median baseline exposure in 1989 was 2.65 [interquartile range (IQR): 2.23-3.33], which generally slightly decreased over time. Using 10 y as a lag-time in our models, the adjusted hazard ratio per each IQR increase of the natural log-transformed cumulative BC was 1.17 (95% confidence interval: 1.06, 1.29) for all-sites cancer combined and 1.31 (0.93, 1.83) for lung cancer. Associations with BC residuals were also positive for both outcomes. Using 2 y as a lag-time, the results were similar. Our findings for a cohort of French adults suggest that BC may partly explain the association between and lung cancer. Additional studies are needed to confirm our results and further disentangle the effects of BC, total , and other constituents. https://doi.org/10.1289/EHP8719.

Epidemiological studies on the health effects of air pollution usually rely on measurements from fixed ground monitors, which provide limited spatio-temporal coverage. Data from satellites, reanalysis, and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolutions. This study aims to develop a multi-stage satellite-based machine learning model to estimate daily fine particulate matter (PM2.5) levels across Great Britain between 2008–2018. This high-resolution model consists of random forest (RF) algorithms applied in four stages. Stage-1 augments monitor-PM2.5 series using co-located PM10 measures. Stage-2 imputes missing satellite aerosol optical depth observations using atmospheric reanalysis models. Stage-3 integrates the output from previous stages with spatial and spatio-temporal variables to build a prediction model for PM2.5. Stage-4 applies Stage-3 models to estimate daily PM2.5 concentrations over a 1 km grid. The RF architecture performed well in all stages, with results from Stage-3 showing an average cross-validated R2 of 0.767 and minimal bias. The model performed better over the temporal scale when compared to the spatial component, but both presented good accuracy with an R2 of 0.795 and 0.658, respectively. These findings indicate that direct satellite observations must be integrated with other satellite-based products and geospatial variables to derive reliable estimates of air pollution exposure. The high spatio-temporal resolution and the relatively high precision allow these estimates (approximately 950 million points) to be used in epidemiological analyses to assess health risks associated with both short- and long-term exposure to PM2.5.

Background The SOPHYA-cohort-study investigated whether the objectively characterized and perceived residential neighborhood of Swiss youth predict accelerometer-measured physical activity and activity in specific domains (participation in a sports club and cycling) five years later. Methods At baseline in 2014, 1230 children and adolescents aged 6 to 16 years participated and wore accelerometers for 7 days. Of these children, 447 participated again in the follow-up study in 2019 and provided longitudinal accelerometer measurements. Sociodemographic factors and perceptions of the local neighbourhood were assessed by questionnaire. Specific objective environmental data (e.g. built environment or social environment) was modelled to the children’s address at baseline. Multivariate linear and logistic regression models were applied to identify short- and long-term characteristics that are associated with accelerometer-based physical activity, cycling and participation in organised sport. Results If the neighborhood-score as perceived by the parents in 2014 was in the middle or lowest tertile, children were significantly less active cross-sectionally in 2014 (-41.1 (-78.0;-4.2) and -52.4 (-88.6;-16.2) counts per minute, cpm), and five years later (-52.4 (-88.6;-16.2) and 48.1 (-86.6;-9.7) cpm). In addition, they were also less likely to accumulate active minutes above the median at both measuring points compared to peers of the same age and sex. Using objective environmental data modeled around the children’s residential address, similar associations were found: In the tertile with the lowest proportion of green space children achieved less cpm in 2014, while a high main street density and a low socioeconomic environment, respectively, hindered physical activity tracking above the median longitudinally. Also for cycling and participation in a sport club, the associations with the perceived and objective environment were more pronounced in the longitudinal analyses. Conclusion The results suggest that growing up in a physical activity friendly neighborhood increases the likelihood of remaining active during adolescence and early adulthood. Interventions should be implemented to ensure that children growing up in an unfavorable neighborhood do not fall behind at an early stage.

Outdoor air pollution is increasingly recognised as a key threat to population health globally, with particularly high risks for urban residents. In this study, we assessed the association between residential nitrogen dioxide (NO2) exposure and children's cognitive and behavioural development using data from São Paulo Brazil, one of the largest urban agglomerations in the world. We used data from the São Paulo Western Region Birth Cohort, a longitudinal cohort study aiming to examine determinants as well as long-term implications of early childhood development. Cross-sectional data from the 72-month follow-up was analysed. Data on NO2 concentration in the study area was collected at 80 locations in 2019, and land use regression modelling was used to estimate annual NO2 concentration at children's homes. Associations between predicted NO2 exposure and children's cognitive development as well as children's behavioural problems were estimated using linear regression models adjusted for an extensive set of confounders. All results were expressed per 10 μg/m3 increase in NO2. 1143 children were included in the analysis. We found no association between NO2 and children's cognitive development (beta -0.05, 95% CI [-0.20; 0.10]) or behavioural problems (beta 0.02, 95% CI [-0.80; 0.12]). No association between child cognition or child behaviour and NO2 was found in this cross-sectional analysis. Further research will be necessary to understand the extent to which these null results reflect a true absence of association or other statistical, biological or adaptive factors not addressed in this paper.

Studies on annual settlement growth have mainly focused on larger cities or incorporated data rarely available in, or applicable to, sparsely populated areas in sub-Saharan Africa, such as aerial photography or night-time light data. The aim of the present study is to quantify settlement growth in rural communities in Burkina Faso affected by industrial mining, which often experience substantial in-migration. A multi-annual training dataset was created using historic Google Earth imagery. Support vector machine classifiers were fitted on Landsat scenes to produce annual land use classification maps. Post-classification steps included visual quality assessments, majority voting of scenes of the same year and temporal consistency correction. Overall accuracy in the four studied scenes ranged between 58.5% and 95.1%. Arid conditions and limited availability of Google Earth imagery negatively affected classification accuracy. Humid study sites, where training data could be generated in proximity to the areas of interest, showed the highest classification accuracies. Overall, by relying solely on freely and globally available imagery, the proposed methodology is a promising approach for tracking fast-paced population dynamics in rural areas where population data is scarce. With the growing availability of longitudinal high-resolution imagery, including data from the Sentinel satellites, the potential applications of the methodology presented will further increase in the future.