Studies have reported significant reductions in air pollutant levels due to the COVID-19 outbreak worldwide global lockdowns. Nevertheless, all of the reports are limited compared to data from the same period over the past few years, providing mainly an overview of past events, with no future predictions. Lockdown level can be directly related to the number of new COVID-19 cases, air pollution, and economic restriction. As lockdown status varies considerably across the globe, there is a window for mega-cities to determine the optimum lockdown flexibility. To that end, firstly, we employed four different Artificial Neural Networks (ANN) to examine the compatibility to the original levels of CO, O₃, NO₂, NO, PM₂.₅, and PM₁₀, for São Paulo City, the current Pandemic epicenter in South America. After checking compatibility, we simulated four hypothetical scenarios: 10%, 30%, 70%, and 90% lockdown to predict air pollution levels. To our knowledge, ANN have not been applied to air pollution prediction by lockdown level. Using a limited database, the Multilayer Perceptron neural network has proven to be robust (with Mean Absolute Percentage Error ∼ 30%), with acceptable predictive power to estimate air pollution changes. We illustrate that air pollutant levels can effectively be controlled and predicted when flexible lockdown measures are implemented. The models will be a useful tool for governments to manage the delicate balance among lockdown, number of COVID-19 cases, and air pollution.

Water pollution is an urgent problem that needs to be controlled via green transformation and the development of the Yangtze River Economic Belt (YREB). Based on the water pollutant discharge and socio-economic database of prefecture-level cities in the YREB from 2011 to 2015, this study explores the spatiotemporal variations in water pollutant discharge in the YREB via two main indicators: chemical oxygen demand (COD) and ammonia nitrogen (NH₃–N). Further, the spatial effects and determinants of water pollutant discharge are quantitatively estimated. The results show that (1) the water pollutant discharge in the YREB has decreased significantly, with the COD and NH₃–N discharge reduced by 10.46% and 10.79%, respectively, and the discharge reduction in the lower reaches was the most prominent; (2) the spatial pattern of water pollutant discharge in the YREB was generally stable and partially improved, and cities with a high rate of water pollutant reduction in the YREB were distributed in the main stream region of the Yangtze River and the intersection of the main stream and tributaries; (3) spatial effects had a significant impact on water pollutant discharge in the YREB, with regional cooperation and economic radiation through environmental management and control initially showing a combined reduction trend in regional water pollutants; and (4) determinants of population size and agricultural economic share declined to varying degrees at the end of the study period, although the urbanization level continued to increase, indicating that urbanization in the YREB occurred too quickly and that water pollutant discharge reduction was limited. However, economic development leading to the deterioration of the water environment was alleviated. In addition, foreign direct investment (FDI) inflows and rapid industrialization processes must be monitored to increase the reduction in characteristic water pollutants.

Ambient air pollution is one of the leading environmental risk factors to human health, largely offsetting economic growth. This study evaluated health burden and cost associated with the short-term and long-term exposure of major air pollutants (fine particulate matter [PM₂.₅] and ozone [O₃]) during 2013–2018. We developed a database of gridded daily and annual PM₂.₅ and O₃ exposure in China at 15 km × 15 km resolution. Then, we estimated the age- and cause-specific premature deaths and quantified related health damage with an age-adjusted value of statistical life (VSL) measure. The health cost estimated in this study captured direct cost, indirect cost and intangible cost of the premature death attributable to ambient PM₂.₅ and O₃. We found that the national premature deaths attributable to long-term and short-term exposure to PM₂.₅ decreased by 15% and 59%, whereas the national premature deaths attributable to long-term and short-term exposure to O₃ increased by 36% and 94%. Despite a 15% reduction of attributable deaths, the health cost attributable to long-term exposure to PM₂.₅ did not change significantly as a result of GDP growth and population aging. On the other hand, the long-term O₃ related health cost in 2018 doubled that in 2013. Our study suggests that while premature deaths fell as a result of China’s clean air actions, the health costs of air pollution remained high. The growing trends of O₃ highlighted the needs for strategies to reduce both PM₂.₅ and O₃ emissions, for the sake of public health and social well-being in China.

Ambient fine particulate matter (PM₂.₅) has been ranked as the sixth leading risk factor globally for death and disability. Modelling methods based on having access to a limited number of monitor stations are required for capturing PM₂.₅ spatial and temporal continuous variations with a sufficient resolution. This study utilized a land use regression (LUR) model with machine learning to assess the spatial-temporal variability of PM₂.₅. Daily average PM₂.₅ data was collected from 73 fixed air quality monitoring stations that belonged to the Taiwan EPA on the main island of Taiwan. Nearly 280,000 observations from 2006 to 2016 were used for the analysis. Several datasets were collected to determine spatial predictor variables, including the EPA environmental resources dataset, a meteorological dataset, a land-use inventory, a landmark dataset, a digital road network map, a digital terrain model, MODIS Normalized Difference Vegetation Index (NDVI) database, and a power plant distribution dataset. First, conventional LUR and Hybrid Kriging-LUR were utilized to identify the important predictor variables. Then, deep neural network, random forest, and XGBoost algorithms were used to fit the prediction model based on the variables selected by the LUR models. Data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were used to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 58% and 89% of PM₂.₅ variations, respectively. When XGBoost algorithm was incorporated, the explanatory power of the models increased to 73% and 94%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed the other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm for estimating the spatial-temporal variability of PM₂.₅ exposures.

With numerous new chemicals introduced into the environment everyday, identification of their potential hazards to the environment and human health is a considerable challenge. Developing adverse outcome pathway (AOP) framework is promising in helping to achieve this goal as it can bring In Vitro testing into toxicity measurement and understanding. To explore the toxic mechanism underlying environmental chemicals via the AOP approach, an integration of adequate experimental data with systems biology understanding is preferred. Here, we describe a novel method to develop reliable and sensible AOPs that relies on chemical-gene interactions, toxicity pathways, molecular regulations, phenotypes, and outcomes information obtained from comparative toxicogenomics database (CTD) and Ingenuity Pathway Analysis (IPA). Using Benzo(a)pyrene (BaP), a highly studied chemical as a stressor, we identified the pivotal IPA toxicity pathways, the molecular initiating event (MIE), and candidate key events (KEs) to structure AOPs in the liver and lung, respectively. Further, we used the corresponding CTD information of multiple typical AHR-ligands, including 2,3,7,8-tetrachlorodibenzoparadioxin (TCDD), valproic acid, quercetin, and particulate matter, to validate our AOP networks. Our approach is likely to speed up AOP development as providing a time- and cost-efficient way to collect all fragmented bioinformation in published studies. It also facilitates a better understanding of the toxic mechanism of environmental chemicals, and potentially brings new insights into the screening of critical paths in the AOP network.

On March 12th, 2020, the WHO declared COVID-19 as a pandemic. The collective impact of environmental and ecosystem factors, as well as biodiversity, on the spread of COVID-19 and its mortality evolution remain empirically unknown, particularly in regions with a wide ecosystem range. The aim of our study is to assess how those factors impact on the COVID-19 spread and mortality by country. This study compiled a global database merging WHO daily case reports with other publicly available measures from January 21st to May 18th, 2020. We applied spatio-temporal models to identify the influence of biodiversity, temperature, and precipitation and fitted generalized linear mixed models to identify the effects of environmental variables. Additionally, we used count time series to characterize the association between COVID-19 spread and air quality factors. All analyses were adjusted by social demographic, country-income level, and government policy intervention confounders, among 160 countries, globally. Our results reveal a statistically meaningful association between COVID-19 infection and several factors of interest at country and city levels such as the national biodiversity index, air quality, and pollutants elements (PM₁₀, PM₂.₅, and O₃). Particularly, there is a significant relationship of loss of biodiversity, high level of air pollutants, and diminished air quality with COVID-19 infection spread and mortality. Our findings provide an empirical foundation for future studies on the relationship between air quality variables, a country’s biodiversity, and COVID-19 transmission and mortality. The relationships measured in this study can be valuable when governments plan environmental and health policies, as alternative strategy to respond to new COVID-19 outbreaks and prevent future crises.

Although many researchers have identified the potential psychological benefits offered by greenness, the association between green space structures and mental disorders is not well understood. The purpose of this study was to identify associations between green space structures and the incidence of bipolar disorder. To this end, we investigated 1,907,776 individuals collected from Taiwan’s National Health Insurance Research Database. After a follow-up investigation from 2005 to 2016, among those with no history of bipolar disorder, 20,548 individuals were further found to be diagnosed with bipolar disorder. A geographic information system and landscape index were used to quantify three indices of green space structures: mean patch area (area and edge), mean fractal dimension index (shape), and mean proximity index (proximity). Additionally, greenness indices, the normalized difference vegetation index, and the enhanced vegetation index were used to confirm the association between greenness and incidence of bipolar disorder. These five indices were used to represent the individual’s exposure according to the township of the hospital that they most frequently visited with symptoms of the common cold. Spearman’s correlation analysis was performed to select variables by considering their collinearity. Subsequently, the frailty model for each index was used to examine the specific associations between those respective indices and the incidence of bipolar disorder by adjusting for related risk factors, such as socioeconomic status, metabolic syndrome, and air pollution. A negative association was identified between the mean patch area and the mean proximity index, and the incidence of bipolar disorder. In contrast, a positive association was found between the mean fractal dimension index and the incidence of bipolar disorder. We observed similar results in sensitivity testing and subgroup analysis. Exposure to green spaces with a larger area, greater proximity, lower complexity, and greener area may reduce the risk of bipolar disorder.

To characterize the emissions of polycyclic aromatic hydrocarbons (PAHs) from residential biomass burning and coal combustion in field environments, smoke samples were collected from the combustion of six types of biomass in heated kangs and four types of coal in traditional stoves and semi-gasifier stoves. The emission factors (EFs) of the total PAH were in the range of 84.5–344 mg/kg for biomass burning, with lower EFs for biomass with higher densities, and in the range of 38.0–206 mg/kg for coal combustion, with lower EFs for coals with higher maturity. Moreover, EFs were lower from high-density biomass fuels (wood trunk, 84.5 ± 11.3 mg/kg) than low-maturity coals (bituminous coal, 206 ± 16.5 mg/kg). Parent, oxygenated, alkylated, and nitrated PAHs accounted for 81.1%, 12.6%, 6.2%, and 0.1%, respectively, of the total-PAH EFs from biomass burning, and 84.7%, 13.8%, 1.4%, and 0.1%, respectively, of the total-PAH EFs from coal combustion. PAH source profiles differed negligibly between biomass fuels but differed significantly between bituminous coal and anthracite coal fuels. The characteristic species of sources were phenanthrene, 9-fluorenone, and 2-nitrobiphenyl for biomass burning, and were phenanthrene, benzo[ghi]perylene, 1,4-naphthoquinone, and 2-nitrobiphenyl for coal combustion. The ratios of benzo[b]fluoranthene/(benzo[b]fluoranthene + benzo[k]fluoranthene) were 0.40–0.45 for biomass burning and 0.89–0.91 for coal combustion, and these significantly different values constitute unique markers for distinguishing these fuels in source apportionment. Benzo[a]pyrene-equivalent factor emissions were 2.79–11.3 mg/kg for biomass and 7.49–41.9 mg/kg for coal, where parent PAHs contributed 92.0%–95.1% from biomass burning and 98.6%–98.8% from coal combustion. Total-PAH emissions from residential heating were 1552 t across Shaanxi province, to which wheat straw (445 t) in biomass burning and bituminous coal (438 t) in coal combustion were the highest contributors. Results from this study provide crucial knowledge for the source identification of PAHs as well as for the design of abatement strategies against pollutant emissions.

As zoned areas of industries, industrial parks have great impacts on the environment. Several studies have demonstrated that chemical compounds and heavy metals released from industrial parks can contaminate soil, water, and air. However, as an emerging pollutant, antimicrobial resistance genes (ARGs) in industrial parks have not yet been investigated. Here, we collected soil samples from 35 sites in an industrial park in China and applied a metagenomics strategy to profile the ARGs and virulence factors (VFs). We further compared the relative abundance of ARGs between the sites (TZ_31–35) located in a beta-lactam antimicrobial-producing factory and other sites (TZ_1–30) in this industrial park. Metagenomic sequencing and assembly generated 14, 383, 065 contigs and 17, 631, 051 open reading frames (ORFs). Taxonomy annotation revealed Proteobacteria and Actinobacteria as the most abundant phylum and class, respectively. The 32 pathogenic bacterial genera listed in the virulence factor database (VFDB) were all identified from the soil metagenomes in this industrial park. In total, 685,354 ARGs (3.89% of the ORFs) and 272,694 virulence factors (VFs) (1.55% of the ORFs) were annotated. These ARGs exhibited resistance to several critically important antimicrobials, such as rifampins, fluroquinolones, and beta-lactams. In addition, no significant difference in the relative abundance of ARGs was observed between sites TZ_31–35 and TZ_1–30, indicating that ARGs have already disseminated widely in this industrial park. The present study gave us a better understanding of the whole picture of the resistome and virulome in the soil of the industrial park and suggested that we should treat the industrial park as a whole in the surveillance and maintenance of ARGs.

Bisphenol A (BPA) is a classical chemical contaminant in food, and the mode of action (MOA) of BPA remains unclear, constraining the progress of risk assessment. This study aims to assess the potential MOAs of BPA regarding reproductive/developmental toxicity, neurological toxicity, and proliferative effects on the mammary gland and the prostate potentially related to carcinogenesis by using the Comparative Toxicogenomics Database (CTD)-based bioinformatics analysis and the quantitative weight of evidence (QWOE) approach on the basis of the principles of Toxicity Testing in the 21st Century. The CTD-based bioinformatics analysis results showed that estrogen receptor 1, estrogen receptor 2, mitogen-activated protein kinase (MAPK) 1, MAPK3, BCL2 apoptosis regulator, caspase 3, BAX, androgen receptor, and AKT serine/threonine kinase 1 could be the common target genes, and the apoptotic process, cell proliferation, testosterone biosynthetic process, and estrogen biosynthetic process might be the shared phenotypes for different target organs. In addition, the KEGG pathways of the BPA-induced action might involve the estrogen signaling pathway and pathways in cancer. After the QWOE evaluation, two potential estrogen receptor-related MOAs of BPA-induced testis dysfunction and learning-memory deficit were proposed. However, the confidence and the human relevance of the two MOAs were moderate, prompting studies to improve the MOA-based risk assessment of BPA.