Apis mellifera is a pollinator insect model in pesticide risk assessment tests for bees. However, given the economic and ecological importance of stingless bees such as Melipona scutellaris in the Neotropical region, as well as the lack of studies on the effect of insecticides on these bees, toxicity tests for stingless bees should be carried out to understand whether insecticides affect both species of bees in the same manner. Thus, the present study quantified the differential sensitivity of the bees M. scutellaris and A. mellifera to the oral ingestion of the insecticide thiamethoxam by determining the mean lethal concentration (LC₅₀), mean lethal time (LT₅₀), and their effect on the insecticide target organ, the brain. The results showed that the stingless bee is more sensitive to the insecticide than A. mellifera, with a lower LC₅₀ of 0.0543 ng active ingredient (a.i.)/μL for the stingless bee compared to 0.227 ng a.i./μL for A. mellifera. When exposed to a sublethal concentration, morphological and ultrastructural analyses were performed and evidenced a significant increase in spaces between nerve cells of both species. Thus, A. mellifera is not the most appropriate or unique model to determine the toxicity of insecticides to stingless bees.

In this work, a sand and dust storm vulnerability mapping (SDS-VM) approach is developed to model the vulnerability of urban blocks to SDS using GIS spatial analysis and a range of geographical data. The SDS-VM was carried out in Ahvaz, IRAN, representing one of the most dust-polluted cities in West Asia. Here, vulnerability is defined as a function of three components: exposure, sensitivity, and adaptive capacity of the people in the city blocks to sand and dust storms. These components were formulated into measurable indicators (i.e. GIS layers) including: PM₂.₅, wind speed, distance from dust emission sources, demographic statistics (age, gender, family size, education level), number of building floors, building age, land surface temperature (LST), land use, percentage of literate population, distance from health services, distance from city facilities (city center, shopping centers), distance from infrastructure (public transportation, main roads and highways), distance from parks and green spaces, and green area per capita. The components and the indicators were weighted using analytical hierarchy process (AHP). Different levels of risks for the components and the indicators were defined using ordered weighted averaging (OWA). Urban SDS vulnerability maps at different risk levels were generated through spatial multi-criteria data analysis procedure. Vulnerability maps, with different risk levels, were validated against field-collected data of 781 patients hospitalized for dust-related diseases (i.e. respiratory, cardiovascular, and skin). Results showed that (i) SDS vulnerability map, obtained from the developed methodology, gives an overall accuracy of 79%; (ii); regions 1 and 5 of Ahvaz are recognized with the highest and lowest vulnerabilities to SDS, respectively; and (iii) ORness equal to 0 (very low risk) is the optimum SDS-VM risk level for decision-making to mitigate the harmful impacts of SDS in the deposition areas of Ahvaz city.

Based on the concentrations of ten heavy metals (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn, Fe) in 144 road dust samples collected from 36 sites across 4 seasons from 2016 to 2017 in Beijing, this study systematically analyzed the levels and main sources of health risks in terms of their temporal and spatial variations. A combination of receptor models (positive matrix factorization and multilinear engine-2), human health risk assessment models, and Monte Carlo simulations were used to apportion the seasonal variation of the health risks associated with these heavy metals. While non-carcinogenic risks were generally acceptable, Cr and Ni induced cautionary carcinogenic risks (CR) to children (confidence levels was approximately 80% and 95%, respectively).. Additionally, fuel combustion posed cautionary CR to children in all seasons, while the level of CR from other sources varied, depending on the seasons. Heavy metal concentrations were the most influential variables for uncertainties, followed by ingestion rate and skin adherence factor. The values and spatial patterns of health risks were influenced by the spatial pattern of risks from each source.

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.