The Lijiang River is of great ecological and environmental importance for Guilin City, which is located in the karst area of southeast China. Given its importance, a detailed evaluation of the heavy metals (HMs) in the river sediment is required. For the first time, 61 sediment samples were collected along the entire Lijiang River to determine pollution level and ecological risk posed by 10 HMs (Co, Cr, Cu, Mn, Ni, Pb, Zn, As, Hg, and Cd). These were assessed using the geo-accumulation index, potential ecological risk index, and modified degree of contamination. The results showed that the mean concentrations of the majority of HMs exceeded their corresponding background values and followed the trend: midstream > downstream > upstream. Based on the spatial distributions and pollution indices of the 10 HMs, the Lijiang River was found to have a high accumulation of Cd, Hg, Zn, and Pb in the sediments. The midstream area was the most polluted with respect to Cd and Hg, and also posed a relatively higher potential ecological risk than the downstream and upstream areas. The sources of the assessed HMs were inferred based on a correlation analysis and principal component analysis, which identified both natural and anthropogenic sources. A higher pollution potential was associated with Cd, Hg, Pb, and Zn in the midstream and downstream areas due to higher organic and carbonate content, urbanization, agricultural activities, and leisure activities (e.g., boating and cruises). In contrast, natural erosion and weathering processes were responsible for the HM concentrations in the upstream area. The findings of this study will help the local authorities to protect the important water resource of the Lijiang River.

Potential health benefits from improved ambient air quality during the COVID-19 shutdown have been recently reported and discussed. Despite the shutdown measures being in place, northern China still suffered severe haze episodes (HE) that are not yet fully understood, particularly how the source emissions changed. Thus, the meteorological conditions and source emissions in processing five HEs occurred in Beijing-Tianjin-Hebei area were investigated by analyzing a comprehensive real-time measurement dataset including air quality data, particle physics, optical properties, chemistry, aerosol lidar remote sensing, and meteorology. Three HEs recorded before the shutdown began were related to accumulated primary pollutants and secondary aerosol formation under unfavorable dispersion conditions. The common “business as usual” emissions from local primary sources in this highly polluted area exceeded the wintertime atmospheric diffusive capacity to disperse them. Thus, an intensive haze formed under these adverse meteorological conditions such as in the first HE, with coal combustion to be the predominant source. Positive responses to the shutdown measures were demonstrated by reduced contributions from traffic and dust during the final two HEs that overlapped the Spring and Lantern Festivals, respectively. Local meteorological dispersion during the Spring Festival was the poorest among the five HEs. Increased residential burning plus fireworks emissions contributed to the elevated PM₂.₅ with the potential of enhancing the HEs. Our results highlight that reductions from shutdown measures alone do not prevent the occurrence of HEs. To further reduce air pollution and thus improve public health, abatement strategies with an emphasis on residential burning are needed.

To evaluate pesticide regulatory standards in agricultural crops, we introduced a regulatory modeling framework that can flexibly evaluate a population’s aggregate exposure risk via maximum residue levels (MRLs) under good agricultural practice (GAP). Based on the structure of the aggregate exposure model and the nature of variable distributions, we optimized the framework to achieve a simplified mathematical expression based on lognormal variables including the lognormal sum approximation and lognormal product theorem. The proposed model was validated using Monte Carlo simulation, which demonstrates a good match for both head and tail ends of the distribution (e.g., the maximum error = 2.01% at the 99th percentile). In comparison with the point estimate approach (i.e., theoretical maximum daily intake, TMDI), the proposed model produced higher simulated daily intake (SDI) values based on empirical and precautionary assumptions. For example, the values at the 75th percentile of the SDI distributions simulated from the European Union (EU) MRLs of 13 common pesticides in 12 common crops were equal to the estimated TMDI values, and the SDI values at the 99th percentile were over 1.6-times the corresponding TMDI values. Furthermore, the model was refined by incorporating the lognormal distributions of biometric variables (i.e., food intake rate, processing factor, and body weight) and varying the unit-to-unit variability factor (VF) of the pesticide residues in crops. This ensures that our proposed model is flexible across a broad spectrum of pesticide residues. Overall, our results show that the SDI is significantly reduced, which may better reflect reality. In addition, using a point estimate or lognormal PF distribution is effective as risk assessments typically focus on the upper end of the distribution.

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.

Developing a biotechnical system with rapid degradation of pesticide is critical for reducing environmental, food security and health risks. Here, we investigated a novel epigenetic mechanism responsible for the degradation of the pesticide atrazine (ATZ) in rice crops mediated by the key component CORONATINE INSENSITIVE 1a (OsCOI1a) in the jasmonate-signaling pathway. OsCOI1a protein was localized to the nucleus and strongly induced by ATZ exposure. Overexpression of OsCOI1a (OE) significantly conferred resistance to ATZ toxicity, leading to the improved growth and reduced ATZ accumulation (particularly in grains) in rice crops. HPLC/Q-TOF-MS/MS analysis revealed increased ATZ-degraded products in the OE plants, suggesting the occurrence of vigorous ATZ catabolism. Bisulfite-sequencing and chromatin immunoprecipitation assays showed that ATZ exposure drastically reduced DNA methylation at CpG context and histone H3K9me2 marks in the upstream of OsCOI1a. The causal relationships between the DNA demethylation (hypomethylatioin), OsCOI1a expression and subsequent detoxification and degradation of ATZ in rice and environment were well established by several lines of biological, genetic and chemical evidence. Our work uncovered a novel regulatory mechanism implicated in the defense linked to the epigenetic modification and jasmonate signaling pathway. It also provided a modus operandi that can be used for metabolic engineering of rice to minimize amounts of ATZ in the crop and environment.

The effects of atmospheric pollution on plant species richness (nₛₚ) are of widespread concern. We carried out a modelling exercise to estimate how nₛₚ in British semi-natural ecosystems responded to atmospheric deposition of nitrogen (Ndₑₚ) and sulphur (Sdₑₚ) between 1800 and 2010. We derived a simple four-parameter equation relating nₛₚ to measured soil pH, and to net primary productivity (NPP), calculated with the N14CP ecosystem model. Parameters were estimated from a large data set (n = 1156) of species richness in four vegetation classes, unimproved grassland, dwarf shrub heath, peatland, and broadleaved woodland, obtained in 2007. The equation performed reasonably well in comparisons with independent observations of nₛₚ. We used the equation, in combination with modelled estimates of NPP (from N14CP) and soil pH (from the CHUM-AM hydrochemical model), to calculate changes in average nₛₚ over time at seven sites across Britain, assuming that variations in nₛₚ were due only to variations in atmospheric deposition. At two of the sites, two vegetation classes were present, making a total of nine site/vegetation combinations. In four cases, nₛₚ was affected about equally by pH and NPP, while in another four the effect of pH was dominant. The ninth site, a chalk grassland, was affected only by NPP, since soil pH was assumed constant. Our analysis suggests that the combination of increased NPP, due to fertilization by Ndₑₚ, and decreased soil pH, primarily due to Sdₑₚ, caused an average species loss of 39% (range 23–100%) between 1800 and the late 20th Century. The modelling suggests that in recent years nₛₚ has begun to increase, almost entirely due to reductions in Sdₑₚ and consequent increases in soil pH, but there are also indications of recent slight recovery from the eutrophying effects of Ndₑₚ.

Polycyclic aromatic compounds (PACs) are ubiquitous across environmental media in Canada, including surface water, soil, sediment and snowpack. Information is presented according to pan-Canadian sources, and key geographical areas including the Great Lakes, the Alberta Oil Sands Region (AOSR) and the Canadian Arctic. Significant PAC releases result from exploitation of fossil fuels containing naturally-derived PACs, with anthropogenic sources related to production, upgrading and transport which also release alkylated PACs. Continued expansion of the oil and gas industry indicates contamination by PACs may increase. Monitoring networks should be expanded, and include petrogenic PACs in their analytical schema, particularly near fuel transportation routes. National-scale roll-ups of emission budgets may not expose important details for localized areas, and on local scales emissions can be substantial without significantly contributing to total Canadian emissions. Burning organic matter produces mainly parent or pyrogenic PACs, with forest fires and coal combustion to produce iron and steel being major sources of pyrogenic PACs in Canada. Another major source is the use of carbon electrodes at aluminum smelters in British Columbia and Quebec. Temporal trends in PAC levels across the Great Lakes basin have remained relatively consistent over the past four decades. Management actions to reduce PAC loadings have been countered by increased urbanization, vehicular emissions and areas of impervious surfaces. Major cities within the Great Lakes watershed act as diffuse sources of PACs, and result in coronas of contamination emanating from urban centres, highlighting the need for non-point source controls to reduce loadings.

Polyethylene terephthalate (PET) is a possible key component of nanoplastics in water environments, which can migrate pollutants through co-transport. In this regard, the co-transport of endocrine disruptors (such as bisphenol A, BPA) by nanoplastics is of emergent concern because of its cytotoxicity/bioaccumulation effects in aquatic organisms. In this work, a computational study is performed to reveal the BPA adsorption mechanism onto PET nanoplastics (nanoPET). It is found that the outer surface of nanoPET has a nucleophilic nature, allowing to increase the mass transfer and intraparticle diffusion into the nanoplastic to form stable complexes by inner and outer surface adsorption. The maximum adsorption energy is similar (even higher) in magnitude with respect to nanostructured adsorbents such as graphene, carbon nanotubes, activated carbon, and inorganic surfaces, indicating the worrying adsorption properties of nanoPET. The adsorption mechanism is driven by the interplay of dispersion (38–49%) and electrostatics effects (43–50%); specifically, dispersion effects dominate the inner surface adsorption, while electrostatics energies dominate the outer surface adsorption. It is also determined that π–π stacking is not a reliable interaction mechanism for aromatics on nanoPET. The formed complexes are also highly soluble, and water molecules behave as non-competitive factors, establishing the high risk of nanoPET to adsorb and migrate pollutants in water ecosystems. Furthermore, the adsorption performance is decreased (but not inhibited) at high ionic strength in salt-containing waters. Finally, these results give relevant information for environmental risk assessment, such as quantitative data and interaction mechanisms for non-biodegradable nanoplastics that establish strong interactions with pollutants in water.

Selenium (Se) and arsenic (As) are toxic contaminants in surface water and drinking water. The human body needs little quantity of Se, but too high dose is not allowed. Metal oxides such as iron oxides were used for adsorption or co-precipitation removal of As from water. However, the regeneration and stability problems of metals oxides sorbents are unsatisfactory , and there is not enough adsorbent for Se removal from water also. We developed the acrylic amine fiber (AAF) for adsorption reomval of Se and As from water and systematically studied the influenced factors. Batch experiments were conducted for investigating the adsorption edges, while column filtration tests were employed for dynamic application edges. At neutral pH, the Langmuir isotherm fittings gave the maximum adsorption capacities of As(V), As(III), Se(VI) and Se(IV) are 270.3, 40.5, 256.4, and 158.7 mg/g, respectively. Effects of co-existing inorganic anions on As(V) and Se(VI) adsorption using AAF gave the order of PO₄³⁻ > SO₄²⁻ > NO₃⁻ > SiO₃²⁻, while different organic acids obey the order of citric acid > oxalic acid > formic acid. Fourier transform infrared analysis showed the PO₄³⁻ and SO₄²⁻ competition mechanisms are electrostatic repulsions, while the competition of organic acids derived from acid-base reaction between the carboxyl group and the amino group. Column filtration and regeneration results showed that the spent AAF can be regenerated using 0.5 mol/L HCl solution and reused with no much decrease of adsorption capacity.

Because of environmental and societal concerns, new strategies are being developed to mitigate the effects of road salt. These include new deicers that are alternatives to or mixtures with the most common road salt, sodium chloride (NaCl), improved techniques and equipment, and biotic mitigation methods. Using outdoor mesocosms, we investigated the impacts of NaCl and two common alternatives, magnesium chloride (MgCl₂) and calcium chloride (CaCl₂) on freshwater communities. We also investigated the mitigation ability of a common macrophyte, Elodea. We hypothesized that road salt exposure reduces filamentous algae, zooplankton, and macrocrustaceans, but results in increases in phytoplankton and gastropods. We also hypothesized that MgCl₂ is the most toxic salt to communities, followed by CaCl₂, and then NaCl. Lastly, we hypothesized that macrophytes mitigate some of the effects of road salt, specifically the effects on primary producers. We found that all three salts reduced filamentous algal biomass and amphipod abundance, but only MgCl₂ reduced Elodea biomass. MgCl₂ had the largest and longest lasting effects on zooplankton, specifically cladocerans and copepods, which resulted in a significant increase in phytoplankton and rotifers. CaCl₂ increased ostracods and decreased snail abundance, but NaCl increased snail abundance. Lastly, while we did not find many interactions between road salt and macrophyte treatments, macrophytes did counteract many of the salt effects on producers, leading to decreased phytoplankton, increased filamentous algae, and altered abiotic responses. Thus, at similar chloride concentrations, NaCl alternatives, specifically MgCl₂, are not safer for aquatic ecosystems and more research is needed to find safer road management strategies to protect freshwater ecosystems.