Association between exposure to particulate matter during pregnancy and multidimensional development in school‐age children: A cross‐sectional study in Italy

Air pollutants can potentially affect the development of children. However, data on the effect of exposure to air pollution during pregnancy and developmental outcomes in school children are rare. We investigated the link between prenatal exposure to particulate matters smaller than 10 microns (PM10) and the development of school-age children in multiple domains. Cross-sectional data were collected in Italy between 2013 and 2014. Children aged between 5 and 8 years (n = 1187) were assessed on cognitive, communication, socio-emotional, adaptive, and motor developmental domains using the Developmental Profile 3 questionnaire. The monthly average concentration of PM10 during the entire fetal period was linked to the municipality of residence of the children. The increase in the prenatal PM10 was associated with a decrease in the cognitive score during the second (+13.2 µg/m3 PM10 increase: −0.30 points; 95%CI: −0.12–−0.48) and third trimesters of pregnancy (−0.31 points; 95%CI: −0.11–−0.50). The communicative domain was also negatively influenced by PM10 increases in the second trimester. The development of cognitive and communicative abilities of children was negatively associated with the exposure to PM10 during the period of fetal development, confirming that exposure to air pollution during pregnancy can potentially hinder the development of the brain

[A cohort study on mortality and morbidity in the area of Taranto, Southern Italy].

Introduction: the area of Taranto has been investigated in several environmental and epidemiological studies due to the presence of many industrial plants and shipyards. Results from many studies showed excesses of mortality and cancer incidence for the entire city of Taranto, but there are no studies for different geographical areas of the city that take into account the important confounding effect of socioeconomic position. Objective: to assess mortality and hospitalization rates of residents in Taranto, Statte and Massafra through a cohort study, with a particular focus on residents in the districts closest to the industrial complex, taking into account the socioeconomic position. Methods: a cohort of residents during the period 1998-2010 was enrolled. Individual follow-up for assessment of vital status at 31.01.2010 was performed using municipality data. The census-tract socioeconomic position level and the district of residence were assigned to each participant, on the basis of the geocoded addresses at the beginning of the follow-up. Standardized cause specific mortality/morbidity rates, adjusted for age, were calculated by gender and districts of residence. Mortality and morbidity Hazard Ratios (HR, CI95%) were calculated by districts and socioeconomic position using Cox models. All models were adjusted for age and calendar period, and were done separately for men and women. Results: 321.356 people were enrolled in the cohort (48.9% males). Mortality/morbidity risks for natural cause, cancers, cardiovascular and respiratory diseases were found to be higher in low socioeconomic position groups compared to high ones. The analyses by districts have shown several excess mortality/morbidity risks for residents in Tamburi (Tamburi, Isola, Porta Napoli and Lido Azzurro), Borgo, Paolo VI and the municipality of Statte. Conclusions: The results of this study showed a significant relationship between socioeconomic position and health status of people resident in Taranto. People living in the districts closest to the industrial zone have higher mortality/morbidity levels compared to the rest of the area also taking into account the socioeconomic position

Monitoring the impact of desert dust outbreaks for air quality for health studies

We review the major features of desert dust outbreaks that are relevant to the assessment of dust impacts upon human health. Our ultimate goal is to provide scientific guidance for the acquisition of relevant population exposure information for epidemiological studies tackling the short and long term health effects of desert dust. We first describe the source regions and the typical levels of dust particles in regions close and far away from the source areas, along with their size, composition, and bio-aerosol load. We then describe the processes by which dust may become mixed with anthropogenic particulate matter (PM) and/or alter its load in receptor areas. Short term health effects are found during desert dust episodes in different regions of the world, but in a number of cases the results differ when it comes to associate the effects to the bulk PM, the desert dust-PM, or non-desert dust-PM. These differences are likely due to the different monitoring strategies applied in the epidemiological studies, and to the differences on atmospheric and emission (natural and anthropogenic) patterns of desert dust around the world. We finally propose methods to allow the discrimination of health effects by PM fraction during dust outbreaks, and a strategy to implement desert dust alert and monitoring systems for health studies and air quality management.The systematic review was funded by WHO with as part of a Grant Agreement with Ministry of Foreign Affairs, Norway. Thanks are also given to the Spanish Ministry for the Ecological Transition for long term support in the last 2 decades to our projects on African dust effects on air quality over Spain; to the Spanish Ministry of Science, Innovation and Universities and FEDER Funds for the HOUSE project (CGL2016-78594-R), and to the Generalitat de Catalunya (AGAUR 2017 SGR41). Carlos Pérez García-Pando acknowledges long-term support from the AXA Research Fund, as well as the support received through the Ramón y Cajal program (grant RYC-2015-18690) of the Spanish Ministry of Science, Innovation and Universities.Peer ReviewedPostprint (published version

A random forest approach to estimate daily particulate matter, nitrogen dioxide, and ozone at fine spatial resolution in Sweden

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 (NO 2 ) and ozone (O 3 ) 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 PM 10 (PM<10 microns), PM 2.5 (PM<2.5 microns), PM 2.5-10 (PM between 2.5 and 10 microns), NO 2 , and O 3 for each squared kilometer of Sweden over the period 2005-2016. Our models were able to describe between 64% (PM 10 ) and 78% (O 3 ) of air pollutant variability in held-out observations, and between 37% (NO 2 ) and 61% (O 3 ) 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 investigation

Improving 3-day deterministic air pollution forecasts using machine learning algorithms

As air pollution is regarded as the single largest environmental health risk in Europe it is important that communication to the public is up to date and accurate and provides means to avoid exposure to high air pollution levels. Long- and short-term exposure to outdoor air pollution is associated with increased risks of mortality and morbidity. Up-to-date information on present and coming days' air quality helps people avoid exposure during episodes with high levels of air pollution. Air quality forecasts can be based on deterministic dispersion modelling, but to be accurate this requires detailed information on future emissions, meteorological conditions and process-oriented dispersion modelling. In this paper, we apply different machine learning (ML) algorithms – random forest (RF), extreme gradient boosting (XGB), and long short-term memory (LSTM) – to improve 1, 2, and 3 d deterministic forecasts of PM10, NOx, and O3 at different sites in Greater Stockholm, Sweden. It is shown that the deterministic forecasts can be significantly improved using the ML models but that the degree of improvement of the deterministic forecasts depends more on pollutant and site than on what ML algorithm is applied. Also, four feature importance methods, namely the mean decrease in impurity (MDI) method, permutation method, gradient-based method, and Shapley additive explanations (SHAP) method, are utilized to identify significant features that are common and robust across all models and methods for a pollutant. Deterministic forecasts of PM10 are improved by the ML models through the input of lagged measurements and Julian day partly reflecting seasonal variations not properly parameterized in the deterministic forecasts. A systematic discrepancy by the deterministic forecasts in the diurnal cycle of NOx is removed by the ML models considering lagged measurements and calendar data like hour and weekday, reflecting the influence of local traffic emissions. For O3 at the urban background site, the local photochemistry is not properly accounted for by the relatively coarse Copernicus Atmosphere Monitoring Service ensemble model (CAMS) used here for forecasting O3 but is compensated for using the ML models by taking lagged measurements into account. Through multiple repetitions of the training process, the resulting ML models achieved improvements for all sites and pollutants. For NOx at street canyon sites, mean squared error (MSE) decreased by up to 60  %, and seven metrics, such as R2 and mean absolute percentage error (MAPE), exhibited consistent results. The prediction of PM10 is improved significantly at the urban background site, whereas the ML models at street sites have difficulty capturing more information. The prediction accuracy of O3 also modestly increased, with differences between metrics. Further work is needed to reduce deviations between model results and measurements for short periods with relatively high concentrations (peaks) at the street canyon sites. Such peaks can be due to a combination of non-typical emissions and unfavourable meteorological conditions, which are rather difficult to forecast. Furthermore, we show that general models trained using data from selected street sites can improve the deterministic forecasts of NOx at the station not involved in model training. For PM10 this was only possible using more complex LSTM models. An important aspect to consider when choosing ML algorithms is the computational requirements for training the models in the deployment of the system. Tree-based models (RF and XGB) require fewer computational resources and yield comparable performance in comparison to LSTM. Therefore, tree-based models are now implemented operationally in the forecasts of air pollution and health risks in Stockholm. Nevertheless, there is big potential to develop generic models using advanced ML to take into account not only local temporal variation but also spatial variation at different stations.</p

Spatial variability of nitrogen dioxide and formaldehydeand 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 residentialexposure of children living in proximity to the largest chipboard industry in Italy and evaluated the reliability of exposureestimates obtained from a number of available models. We obtained passive sampling data on NO2and formaldehyde collectedby the Environmental Protection Agency of Lombardy region at 25 sites in the municipality of Viadana during 10 weeks (2017-2018) and compared NO2measurements with average weekly concentrations from continuous monitors. We compared interpo-lated NO2and formaldehyde surfaces with previous maps for 2010. We assessed the relationship between residential proximity tothe industry and pollutant exposures assigned using these maps, as well as other available countrywide/continental models basedon routine data on NO2, PM10, andPM2.5. The correlation between NO2concentrations from continuous and passive samplingwas high (Pearson'sr= 0.89), although passive sampling underestimated NO2especially during winter. For both 2010 and 2017-2018, we observed higher NO2and formaldehyde concentrations in the south of Viadana, with hot-spots in proximity to theindustry. PM10and PM2.5exposures were higher for children at 3.5 km to theindustry, whereas NO2exposure was higher at 1-1.7 km to the industry. Road and population densities were also higher close tothe industry. Findings from a variety of exposure models suggest that children living in proximity to the chipboard industry inViadana are more exposed to air pollution and that exposure gradients are relatively stable over time