Although pollutants pose environmental and human health risks, the majority are not routinely monitored and regulated. Organic pollutants emanate from a variety of sources, and can be classified depending on their chemistry and environmental fate. Classification of pollutants is important because it informs fate processes and apposite removal technologies. The occurrence of emerging contaminants (ECs) in water bodies is a source of environmental and human health concern globally. Despite being widely reported, data on the occurrence of ECs in South Africa are scarce. Specifically, ECS in wastewater in the Northern Cape in South Africa are understudied. In this study, various ECs were screened in water samples collected from three wastewater treatment plants (WWTPs) in the province. The ECs were detected using liquid chromatography coupled to high resolution Orbitrap mass spectrometry following Oasis HLB solid-phase extraction. The main findings were: (1) there is a wide variety of ECs in the WWTPs, (2) physico-chemical properties such as pH, total dissolved solids, conductivity, and dissolved organic content showed reduced values in the outlet compared to the inlet which confirms the presence of less contaminants in the treated wastewater, (3) specific ultraviolet absorbance of less than 2 was observed in the WWTPs samples, suggesting the presence of natural organic matter (NOM) that is predominantly non-humic in nature, (4) most of the ECs were recalcitrant to the treatment processes, (5) pesticides, recreational drugs, and analgesics constitute a significant proportion of pollutants in wastewater, and (6) NOM removal ranged between 35 and 90%. Consequently, a comprehensive database of ECs in wastewater in Sol Plaatje Municipality was created. Since the detected ECs pose ecotoxicological risks, there is a need to monitor and quantify ECs in WWTPs. These data are useful in selecting suitable monitoring and control strategies at WWTPs.

Most amyotrophic lateral sclerosis (ALS) cases are sporadic (∼90%) and environmental exposures are implicated in their etiology. Large industrial facilities are permitted the airborne release of certain chemicals with hazardous properties and report the amounts to the US Environmental Protection Agency (EPA) as part of its Toxics Release Inventory (TRI) monitoring program. The objective of this project was to identify industrial chemicals released into the air that may be associated with ALS etiology. We geospatially estimated residential exposure to contaminants using a de-identified medical claims database, the SYMPHONY Integrated Dataverse®, with ∼26,000 nationally distributed ALS patients, and non-ALS controls matched for age and gender. We mapped TRI data on industrial releases of 523 airborne contaminants to estimate local residential exposure and used a dynamic categorization algorithm to solve the problem of zero-inflation in the dataset. In an independent validation study, we used residential histories to estimate exposure in each year prior to diagnosis. Air releases with positive associations in both the SYMPHONY analysis and the spatio-temporal validation study included styrene (false discovery rate (FDR) 5.4e-5), chromium (FDR 2.4e-4), nickel (FDR 1.6e-3), and dichloromethane (FDR 4.8e-4). Using a large de-identified healthcare claims dataset, we identified geospatial environmental contaminants associated with ALS. The analytic pipeline used may be applied to other diseases and identify novel targets for exposure mitigation. Our results support the future evaluation of these environmental chemicals as potential etiologic contributors to sporadic ALS risk.

Petroleum hydrocarbon pollution is a global problem. However, the effects of different petroleum pollution levels on soil microbial communities and ecological functions are still not clear. In this study, we analyzed the changes in microbial community structures and carbon and nitrogen transformation functions in oil-contaminated soils at different concentrations by chemical analysis, high-throughput sequencing techniques, cooccurrence networks, and KEGG database comparison functional gene annotation. The results showed that heavy petroleum concentrations (petroleum concentrations greater than 20,000 mg kg⁻¹) significantly decreased soil microbial diversity (p = 0.01), soil microbiome network complexity, species coexistence patterns, and prokaryotic carbon and nitrogen fixation genes. In medium petroleum contamination (petroleum concentrations of between 4000 mg kg⁻¹ and 20,000 mg kg⁻¹), microbial diversity (p > 0.05) and carbon and nitrogen transformation genes showed no evident change but promoted species coexistence patterns. Heavy petroleum contamination increased the Proteobacteria phylum abundance by 3.91%–57.01%, while medium petroleum contamination increased the Actinobacteria phylum abundance by 1.69%–0.26%. The results suggested that petroleum concentrations played a significant role in shifting soil microbial community structures, ecological functions, and species diversities.

Microplastics are a pollutant of growing concern, capable of harming aquatic organisms and entering the food web. While freshwater microplastic research has expanded in recent years, much remains unknown regarding the sources and delivery pathways of microplastics in these environments. This review aims to address the scientific literature regarding the spatial and temporal factors affecting global freshwater microplastic distributions and abundances. A total of 75 papers, published through June 2021 and containing an earliest publication date of October 2014, was identified by a Web of Science database search. Microplastic spatial distributions are heavily influenced by anthropogenic factors, with higher concentrations reported in regions characterized by urban land cover, high population density, and wastewater treatment plant effluent. Spatial distributions may also be affected by physical watershed characteristics such as slope and elevation (positive and negative correlations with microplastic concentrations, respectively), although few studies address these factors. Temporal variables of influence include precipitation and stormwater runoff (positive correlations) and water flow/discharge (negative correlations). Despite these overarching trends, variations in study results may be due to differing scales or contributing area delineations. Thus, more rigorous and standardized spatial analytical methods are needed. Future research could simultaneously evaluate both spatial and temporal factors and incorporate finer temporal resolutions into sampling campaigns.

Increased interest in backyard food production has drawn attention to the risks associated with urban trace element contamination, in particular lead (Pb) that was used in abundance in Pb-based paints and gasoline. Here we examine the sources, pathways and risks associated with environmental Pb in urban gardens, domestic chickens and their eggs. A suite of other trace element concentrations (including As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn) are reported from the sampled matrices. Sixty-nine domestic chickens from 55 Sydney urban gardens were sampled along with potential sources (feed, soil, water), blood Pb concentrations and corresponding concentrations in eggs. Age of the sampled chickens and house age was also collected. Commercial eggs (n = 9) from free range farms were analysed for comparative purposes. Study outcomes were modelled using the large Australian VegeSafe garden soil database (>20,000 samples) to predict which areas of inner-city Sydney, Melbourne and Brisbane are likely to have soil Pb concentrations unsuitable for keeping backyard chickens. Soil Pb concentrations was a strong predictor of chicken blood and egg Pb (p=<0.00001). Almost 1 in 2 (n = 31/69) chickens had blood Pb levels >20 μg/dL, the level at which adverse effects may be observed. Older homes were correlated with higher chicken blood Pb (p = 0.00002) and egg Pb (p = 0.005), and younger chickens (<12 months old) had greater Pb concentrations, likely due to increased Pb uptake during early life development. Two key findings arose from the study data: (i) in order to retain chicken blood Pb below 20 μg/dL, soil Pb needs to be < 166 mg/kg; (ii) to retain egg Pb < 100 μg/kg (i.e. a food safety benchmark value), soil Pb needs to be < 117 mg/kg. These concentrations are significantly lower than the soil Pb guideline of 300 mg/kg for residential gardens. This research supports the conclusion that a large number of inner-city homes may not be suitable for keeping chickens and that further work regarding production and consumption of domestic food is warranted.

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.