Quantifying the contribution of environmental variables to cyclists' exposure to PM2.5 using machine learning techniques.

Cyclists are particularly vulnerable to travel-related exposure to air pollution. Understanding the factors that increase exposure is crucial for promoting healthier urban environments. Machine learning models have successfully predicted air pollutant concentrations, but they tend to be less interpretable than classical statistical ones, such as linear models. This study aimed to develop a predictive model to assess cyclists' exposure to fine particulate matter (PM2.5) in urban environments. The model was generated using geo-temporally referenced data and machine learning techniques. We explored several models and found that the gradient boosting machine learning model best fitted the PM2.5 predictions, with a minimum root mean square error value of 5.62 µg m-3. The variables with greatest influence on cyclist exposure were the temporal ones (month, day of the week, and time of the day), followed by meteorological variables, such as temperature, relative humidity, wind speed, wind direction, and atmospheric pressure. Additionally, we considered relevant attributes, which are partially linked to spatial characteristics. These attributes encompass street typology, vegetation density, and the flow of vehicles on a particular street, which quantifies the number of vehicles passing a given point per minute. Mean PM2.5 concentration was lower in bicycle paths away from vehicular traffic than in bike lanes along streets. These outcomes underscore the need to thoughtfully design public transportation routes, including bus routes, concerning the network of bicycle pathways. Such strategic planning attempts to improve the air quality in urban landscapes.

Indoor persistent organic pollutants in agricultural areas from Argentina.

Persistent Organic Pollutants (POPs) are anthropogenic chemicals extensively used in the past for industrial and agricultural purposes, characterized by their lipophilicity, ubiquity, volatility and environmental persistence. By other hand, chlorpyrifos is the most widely used current pesticide (CUPs) being the main insecticide used for crops in Argentina. The aim of this work was to assess levels of POPs and CUPs in different fractions of airborne particles collected indoor in agricultural areas from Argentina. Particles higher than 2.5 µm were trapped in polyurethane foams (PUF) while particles smaller than 1 µm and volatile compounds were adsorbed on activated charcoal. Compounds were analyzed by gas chromatograph with electron capture detector (GC-ECD). Endosulfans, chlordanes, PCBs, and HCHs were detected in all PUF samples, while endosulfans, chlorpyrifos, PCBs, and HCHs were the most abundant in smaller particles. Majority of pesticides showed higher concentrations during the summer season (1397.7 vs 832.5 pg/m3 ). Even adding up all measured organic compounds, no sample reaches the threshold limit value for indoor pesticides levels (0.1 pg/m3 ), neither in the large or small particle fraction. However, the fact that chronic exposure to POPs has been linked to several diseases raises concern for human health.