A simple approach for ranking the leaching of pesticides from surface soil presented and tentatively calibrated with field data from an agricultural a The approach is based on the calculation of a leaching index indicating the proportion of active ingredient, with respect to the quantity applied, leac from a soil model in a given time interval (one year). In the selected area wells tapping an unconfined aquifer were sampled for groundwater pesticide residue analysis, in order to explore the index region between leachers and nonleachers.

The relation between pesticides exposure and metabolic syndrome (MetS) has not been clearly identified. Performing a systematic review and meta-analysis, PubMed, Cochrane Library, Embase, and ScienceDirect were searched for studies reporting the risk of MetS following pesticides exposure and their contaminants. We included 12 studies for a total of 6789 participants, in which 1981 (29.1%) had a MetS. Overall exposure to pesticides and their contaminants increased the risk of MetS by 30% (95CI 22%–37%). Overall organochlorine increased the risk of MetS by 23% (14–32%), as well as for most types of organochlorines: hexachlorocyclohexane increased the risk by 53% (28–78%), hexachlorobenzene by 40% (0.01–80%), dichlorodiphenyldichloroethylene by 22% (9–34%), dichlorodiphenyltrichloroethane by 28% (5–50%), oxychlordane by 24% (1–47%), and transnonchlor by 35% (19–52%). Sensitivity analyses confirmed that overall exposure to pesticides and their contaminants increased the risk by 46% (35–56%) using crude data or by 19% (10–29%) using fully-adjusted model. The risk for overall pesticides and types of pesticides was also significant with crude data but only for hexachlorocyclohexane (36% risk increase, 17–55%) and transnonchlor (25% risk increase, 3–48%) with fully-adjusted models. Metaregressions demonstrated that hexachlorocyclohexane increased the risk of MetS in comparison to most other pesticides. The risk increased for more recent periods (Coefficient = 0.28, 95CI 0.20 to 0.37, by year). We demonstrated an inverse relationship with body mass index and male gender. In conclusion, pesticides exposure is a major risk factor for MetS. Besides organochlorine exposure, data are lacking for other types of pesticides. The risk increased with time, reflecting a probable increase of the use of pesticides worldwide. The inverse relationship with body mass index may signify a stockage of pesticides and contaminants in fat tissue.

Ground-based Multi-Axis Differential Optical Absorption Spectroscopy (Max-DOAS) measurements of nitrogen dioxide (NO₂) were continuously obtained from January to November 2019 in northeastern China (NEC). Seasonal variations in the mean NO₂ vertical column densities (VCDs) were apparent, with a maximum of 2.9 × 10¹⁶ molecules cm⁻² in the winter due to enhanced NO₂ emissions from coal-fired winter heating, a longer photochemical lifetime and atmospheric transport. Daily maximum and minimum NO₂ VCDs were observed, independent of the season, at around 11:00 and 13:00 local time, respectively, and the most obvious increases and decreases occurred in the winter and autumn, respectively. The mean diurnal NO₂ VCDs at 11:00 increased to at 08:00 by 1.6, 5.8, and 6.7 × 10¹⁵ molecules cm⁻² in the summer, autumn and winter, respectively, due to increased NO₂ emissions, and then decreased by 2.8, 4.2, and 5.1 × 10¹⁵ molecules cm⁻² at 13:00 in the spring, summer, and autumn, respectively. This was due to strong solar radiation and increased planetary boundary layer height. There was no obvious weekend effect, and the NO₂ VCDs only decreased by about 10% on the weekends. We evaluated the contributions of emissions and transport in the different seasons to the NO₂ VCDs using a generalized additive model, where the contributions of local emissions to the total in the spring, summer, autumn, and winter were 89 ± 12%, 92 ± 11%, 86 ± 12%, and 72 ± 16%, respectively. The contribution of regional transport reached 26% in the winter, and this high contribution value was mainly correlated with the northeast wind, which was due to the transport channel of air pollutants along the Changbai Mountains in NEC. The NO₂/SO₂ ratio was used to identify NO₂ from industrial sources and vehicle exhaust. The contribution of industrial NO₂ VCD sources was >66.3 ± 16% in Shenyang due to the large amount of coal combustion from heavy industrial activity, which emitted large amounts of NO₂. Our results suggest that air quality management in Shenyang should consider reductions in local NO₂ emissions from industrial sources along with regional cooperative control.

Many cities across the world face the challenge of severe fine particulate matter (PM₂.₅) pollution. Among the many factors that affect PM₂.₅ pollution, there is an increasing interest in the impacts of urban structure. However, quantifying these impacts in China has been difficult due to differences of study area and scale in existing research, as well as limited sample sizes. Here, we conducted a continental study focusing on 301 prefectural cities in mainland China to investigate the effects of urban structure, including urban size and urban compactness, on PM₂.₅ concentrations. Based on PM₂.₅ raster and land cover data, we used quantile regression and a general multilinear model to estimate the effects and relative contributions of urban size and urban compactness on urban PM₂.₅ pollution, with explicit consideration for pollution level, urban size and geographical location. We found: (1) nationwide, the larger and more compact that cities were, the heavier the PM₂.₅ pollution tended to be. Additionally, this relationship became stronger with increasing levels of pollution. (2) In general, urban size played a more important role than urban form, and there were no significant interactive effects between the two metrics on urban PM₂.₅ concentrations at the national scale. (3) The impacts of urban size and form varied by city size and geographical location. The impacts of urban size were only significant for small or medium-large cities but not for large cities. Among large cities, only urban form had a significantly positive effect on urban PM₂.₅ concentrations. The further north and west that cities were, the more dependent PM₂.₅ pollution was on urban form, whereas the further south and east that cities were, the greater the impact of urban size. These results provide insights into how urban design and planning can be used to alleviate air pollution.

The concentration of persistent organic pollutants (POPs) makes remarkable difference to environmental fate. In the field of passive sampling, the partition coefficients between polystyrene-divinylbenzene resin (XAD) and air (i.e., KXAD₋A) are indispensable to obtain POPs concentration, and the KXAD₋A is generally thought to be governed by temperature and molecular structure of POPs. However, experimental determination of KXAD₋A is unrealistic for countless and novel chemicals. Herein, the Abraham solute descriptors of poly parameter linear free energy relationship (pp-LFER) and temperature were utilized to develop models, namely pp-LFER-T, for predicting KXAD₋A values. Two linear (MLR and LASSO) and four nonlinear (ANN, SVM, kNN and RF) machine learning algorithms were employed to develop models based on a data set of 307 sample points. For the aforementioned six models, R²ₐdⱼ and Q²ₑₓₜ were both beyond 0.90, indicating distinguished goodness-of-fit and robust generalization ability. By comparing the established models, the best model was observed as the RF model with R²ₐdⱼ = 0.991, Q²ₑₓₜ = 0.935, RMSEₜᵣₐ = 0.271 and RMSEₑₓₜ = 0.868. The mechanism interpretation revealed that the temperature, size of molecules and dipole-type interactions were the predominant factors affecting KXAD₋A values. Concurrently, the developed models with the broad applicability domain provide available tools to fill the experimental data gap for untested chemicals. In addition, the developed models were helpful to preliminarily evaluate the environmental ecological risk and understand the adsorption behavior of POPs between XAD membrane and air.