Heavy metal toxicity has become an impediment to agricultural productivity, which presents major human health concerns in terms of food safety. Among them, arsenic (As) a non-essential heavy metal has gained worldwide attention because of its noxious effects on agriculture and public health. The increasing rate of global warming and anthropogenic activities have promptly exacerbated As levels in the agricultural soil, thereby causing adverse effects to crop genetic and phenotypic traits and rendering them vulnerable to other stresses. Conventional breeding and transgenic approaches have been widely adapted for producing heavy metal resilient crops; however, they are time-consuming and labor-intensive. Hence, finding new mitigation strategies for As toxicity would be a game-changer for sustainable agriculture. One such promising approach is harnessing plant microbiome in the era of ‘omics’ which is gaining prominence in recent years. The use of plant microbiome and their cocktails to combat As metal toxicity has gained widespread attention, because of their ability to metabolize toxic elements and offer an array of perquisites to host plants such as increased nutrient availability, stress resilience, soil fertility, and yield. A comprehensive understanding of below-ground plant-microbiome interactions and their underlying molecular mechanisms in exhibiting resilience towards As toxicity will help in identifying elite microbial communities for As mitigation. In this review, we have discussed the effect of As, their accumulation, transportation, signaling, and detoxification in plants. We have also discussed the role of the plant microbiome in mitigating As toxicity which has become an intriguing research frontier in phytoremediation. This review also provides insights on the advancements in constructing the beneficial synthetic microbial communities (SynComs) using microbiome engineering that will facilitate the development of the most advanced As remedial tool kit in sustainable agriculture.

Rapid urbanization and the aggregation of human activities in cities have resulted in large amounts of anthropogenic heat (AH) emission, which affects urban climate. Quantifying and assessing the AH emission values accurately and analyzing their spatial distribution characteristics is important to understand the energy exchange processes of urban areas. In this study, the high spatial resolution anthropogenic heat flux (AHF) quantification and spatial distribution analysis were conducted using multi-source data in the Beijing-Tianjin-Hebei region (BTH region) of China. First, the AH emission in district and city level were estimated using inventory method based on energy consumption and socio-economic statistical data; Then, AHF spatial quantification models were constructed based on high spatial resolution nighttime light (NTL) data and Point of interests (POI) data, and 130 m × 130 m gridded AHF quantification result in BTH region was realized; Finally, the potential numerical and spatial distribution patterns of AHF were analyzed using various indicators including contribution rate and aggregation index. The results show that: (1) The parameterized index constructed based on NTL and POI data shows a strong correlation with AHF, with R² ranging from 0.79 to 0.94 and a mean absolute error (MAE) value of 0.72 w·m⁻², which can be applied to the quantification of gridded AHF values with high resolution. The highest total AHF in the study area is 214 w·m⁻², and the average value is 2.24 w·m⁻². (2) Considering the sources of AHF, industrial emission sources in BTH region contribute the most to the total AHF, but commercial building emission sources in Beijing have a higher contribution, which can reach 33.8%. (3) Different types of AHF have different spatial aggregation levels. Commercial building emission and human metabolic emission have the highest aggregation level, and transportation emission has the lowest aggregation level.

Mulch film (MF) residues is an important source of microplastics (MPs) in farmland, but its transportation risk to the wider environment was still unknown. Some researches have pursued the sources of MPs found in exorheic rivers. Even so, a systematic study depicting the occurrence, source and fate of microplastics derived from mulch films (MPMF), the crucial component of MPs in farmlands, in exorheic rivers still lacking. Here, the combination of UV–Vis Raman spectroscopy and X-ray photoelectron spectroscopy (XPS) was used to identify the full-size MPMF (1–5000 μm) in field sediment samples collected by single-diagonal systematic sampling. This study verified that MPMF, a polyethylene-matrix composite doped with additives, contributed a considerable part of MPs detected in upstream farmland soil and riverine sediments, and even had an abundance of 38 ± 11 items/kg to 82 ± 15 items/kg, accounting for 9.0%–13.7% of the total MPs in estuary sediments. Notably, upstream farmland was identified to the main source of the riverine MPMF by partial least square path modeling (PLS-PM), contributing to 94.7% of MPMF in riverside sediments and 85.0% of MPMF in estuary sediments. Our study first demonstrates that MPMF constitutes a non-negligible component of MPs in estuarine sediments and underlines the urgency of strengthening the management of MPs pollution in drainage areas with a high agricultural intensity.

This study aims to investigate the effect of transportation infrastructure on the decrease of NO₂ air pollution during three COVID-19-induced lockdowns in a vast region of France. For this purpose, using Sentinel-5P satellite data, the relative change in tropospheric NO₂ air pollution during the three lockdowns was calculated. The estimation of regional infrastructure intensity was performed using Kernel Density Estimation, being the predictor variable. By performing hotspot–coldspot analysis on the relative change in NO₂ air pollution, significant spatial clusters of decreased air pollution during the three lockdowns were identified. Based on the clusters, a novel spatial index, the Clustering Index (CI) was developed using its Coldspot Clustering Index (CCI) variant as a predicted variable in the regression model between infrastructure intensity and NO₂ air pollution decline. The analysis revealed that during the three lockdowns there was a strong and statistically significant relationship between the transportation infrastructure and the decline index, CCI (r = 0.899, R² = 0.808). The results showed that the largest decrease in NO₂ air pollution was recorded during the first lockdown, and in this case, there was the strongest inverse correlation with transportation infrastructure (r = −0.904, R² = 0.818). Economic and population predictors also explained with good fit the decrease in NO₂ air pollution during the first lockdown: GDP (R² = 0.511), employees (R² = 0.513), population density (R² = 0.837). It is concluded that not only economic-population variables determined the reduction of near-surface air pollution but also the transportation infrastructure. Further studies are recommended to investigate other pollutant gases as predicted variables.

Responses to COVID-19 altered environmental exposures and health behaviours associated with non-communicable diseases. We aimed to (1) quantify changes in nitrogen dioxide (NO₂), noise, physical activity, and greenspace visits associated with COVID-19 policies in the spring of 2020 in Barcelona (Spain), Vienna (Austria), and Stockholm (Sweden), and (2) estimated the number of additional and prevented diagnoses of myocardial infarction (MI), stroke, depression, and anxiety based on these changes. We calculated differences in NO₂, noise, physical activity, and greenspace visits between pre-pandemic (baseline) and pandemic (counterfactual) levels. With two counterfactual scenarios, we distinguished between Acute Period (March 15th – April 26th, 2020) and Deconfinement Period (May 2nd – June 30th, 2020) assuming counterfactual scenarios were extended for 12 months. Relative risks for each exposure difference were estimated with exposure-risk functions. In the Acute Period, reductions in NO₂ (range of change from −16.9 μg/m³ to −1.1 μg/m³), noise (from −5 dB(A) to −2 dB(A)), physical activity (from −659 MET*min/wk to −183 MET*min/wk) and greenspace visits (from −20.2 h/m to 1.1 h/m) were largest in Barcelona and smallest in Stockholm. In the Deconfinement Period, NO₂ (from −13.9 μg/m³ to −3.1 μg/m³), noise (from −3 dB(A) to −1 dB(A)), and physical activity levels (from −524 MET*min/wk to −83 MET*min/wk) remained below pre-pandemic levels in all cities. Greatest impacts were caused by physical activity reductions. If physical activity levels in Barcelona remained at Acute Period levels, increases in annual diagnoses for MI (mean: 572 (95% CI: 224, 943)), stroke (585 (6, 1156)), depression (7903 (5202, 10,936)), and anxiety (16,677 (926, 27,002)) would be anticipated. To decrease cardiovascular and mental health impacts, reductions in NO₂ and noise from the first COVID-19 surge should be sustained, but without reducing physical activity. Focusing on cities’ connectivity that promotes active transportation and reduces motor vehicle use assists in achieving this goal.

Urban stormwater is an important pathway for transporting anthropogenic pollutants to water bodies. Phthalate esters (PAEs) are endocrine disruptors owing to their estrogenic activity and potential carcinogenicity and their ubiquitous presence has garnered global interest. However, their transportation by urban stormwater has been largely overlooked. This study, for the first time, investigated 15 PAEs in stormwater from six major stormwater drains in the highly urbanized Hong Kong, a major metropolitan city in China. The results showed that PAEs were ubiquitous in the stormwater of Hong Kong, with total concentrations (∑₁₅PAEs) spanning from 195 to 80,500 ng/L. Bis(2-n-butoxyethyl) phthalate (DBEP), diisopentyl phthalate (DiPP), dicyclohexyl phthalate (DCHP) and di-n-pentyl phthalate (DnPP) were detected in stormwater for the first time. Spatial variations in PAEs were observed among different stormwater drains, possibly due to the different land use patterns and intensities of human activities in their respective catchments. The highest and lowest levels of ∑₁₅PAEs were found in Kwai Chung (3860 ± 1960 ng/L) and the Ng Tung River (672 ± 557 ng/L), respectively. Additionally, significantly higher concentrations of ∑₁₅PAEs in stormwater were found in the wet season (2520 ± 2050 ng/L) than in the dry season (947 ± 904 ng/L). Principal component analysis classified domestic and industrial origins as two important sources of PAEs in the stormwater of Hong Kong. Stormwater played a crucial role in transporting PAEs, with an estimated annual flux of 0.705–29.4 kg. Thus, possible stormwater management measures were proposed to protect the receiving environment and local ecosystems from stormwater.

Studies have indicated that up to 47% of total N fertilizer applied in flooded rice fields may be lost to the atmosphere through NH₃ volatilization. The volatilized NH₃ represents monetary loss and contributes to increase in formation of PM₂.₅ in the atmosphere, eutrophication in surface water, and degrades water and soil quality. The NH₃ is also a precursor to N₂O formation. Thus, it is important to monitor NH₃ volatilization from fertilized and flooded rice fields. Commercially available samplers offer ease of transportation and installation, and thus, may be considered as NH₃ absorbents for the static chamber method. Hence, the objective of this study is to investigate the use of a commercially available NH₃ sampler/absorbent (i.e., Ogawa® passive sampler) for implementation in a static chamber. In this study, forty closed static chambers were used to study two factors (i.e., trapping methods, exposure duration) arranged in a Randomized Complete Block Design. The three trapping methods are standard boric acid solution, Ogawa® passive sampler with acid-coated pads and exposed coated pads without casing. The exposure durations are 1 and 4 h. Results suggest that different levels of absorbed NH₃ was obtained for each of the trapping methods. Highest level of NH₃ was trapped by the standard boric acid solution, followed by the exposed acid-coated pads without casing, and finally acid-coated pads with protective casing, given the same exposure duration. The differences in absorbed NH₃ under same conditions does not warrant direct comparison across the different trapping methods. Any three trapping methods can be used for conducting studies to compare multi-treatments using the static chamber method, provided the same trapping method is applied for all chambers.

Black carbon (BC) aerosol negatively affects air quality and contributes to climate warming globally. However, little is known about the relative contributions of different source control measures to BC reduction owing to the lack of powerful source-diagnostic tools. We combine the fingerprints of dual-carbon isotope using an optimized Bayesian Markov chain Monte Carlo (MCMC) scheme and for the first time to study the key sources of BC in megacity Guangzhou of the Pearl River Delta (PRD) region, China in 2018 autumn season. The MCMC model-derived source apportionment of BC shows that the dominant contributor is petroleum combustion (39%), followed by coal combustion (34%) and biomass burning (27%). It should be noted that the BC source pattern is highly sensitive to the variations of air masses transported with an enhanced contribution of fossil source from the eastern area, suggesting the important impact of regional atmospheric transportation on the BC source profile in the PRD region. Also, we further found that fossil fuel combustion BC contributed 84% to the total BC reduction during 2013–2018. The response of PM₂.₅ concentration to the ¹⁴C-derived BC source apportionment is successfully fitted (r = 0.90) and the results predicted that it would take ∼6 years to reach the WHO PM₂.₅ guideline value (10 μg m⁻³) for the PRD region if the emission control measures keep same as they are at present. Taken together, our findings suggest that dual-carbon isotope is a powerful tool in constraining the source apportionment of BC for the evaluations of air pollution control and carbon emission measures.

Arsenic (As) is one of the most toxic and cancer-causing metals which is generally entered the food chain via intake of As contaminated water or food and harmed the life of living things especially human beings. Therefore, the reduction of As content in the food could be of great importance for healthy life. To reduce As contamination in the soil and food, the evaluation of plant-based As uptake and transportation mechanisms is critically needed. Different soil factors such as physical and chemical properties of soil, soil pH, As speciation, microbial abundance, soil phosphates, mineral nutrients, iron plaques and roots exudates effectively regulate the uptake and accumulation of As in different parts of plants. The detoxification mechanisms of As in plants depend upon aquaporins, membrane channels and different transporters that actively control the influx and efflux of As inside and outside of plant cells, respectively. The xylem loading is responsible for long-distance translocation of As and phloem loading involves in the partitioning of As into the grains. However, As detoxification mechanism based on the clear understandings of how As uptake, accumulations and translocation occur inside the plants and which factors participate to regulate these processes. Thus, in this review we emphasized the different soil factors and plant cell transporters that are critically responsible for As uptake, accumulation, translocation to different organs of plants to clearly understand the toxicity reasons in plants. This study could be helpful for further research to develop such strategies that may restrict As entry into plant cells and lead to high crop yield and safe food production.

China produces and consumes large quantities of brominated flame retardants (BFRs) as well as several other unregulated electronic waste recycling activities, causing high BFR concentrations in the natural environment. Thus, Traditional Chinese Medicines (TCMs) may be contaminated by legacy BFRs (e.g. polybrominated diphenyl ethers (PBDEs)) and emerging BFRs (eBFRs, such as decabromodiphenyl ethane (DBDPE)) during growth, processing, packaging, and transportation. Pheretima, which is a typical animal drug recorded in Chinese Pharmacopoeia, was used as an example to evaluate human exposure to BFRs through TCM intake. This study is the first to determine 25 PBDEs and 5 eBFRs in Pheretima and estimate the daily BFR intake via Pheretima-containing TCMs. Twenty-seven Shanghai Pheretima and fifty-one Guang Pheretima samples were collected between March and June 2019 in southeast China. High BFR detection frequencies were found in Pheretima, of which BDE-209 and DBDPE were the most predominant analytes. The total PBDE contents ranged from 73 pg/g to 8,725 pg/g, while that of the eBFRs varied between 115 pg/g and 2,824 pg/g. The profiles and abundances were found to be species- and origin-dependent. However, the traditional processing of Pheretima may reduce BFR residues. Based on the usual clinical doses of Pheretima and the available chronic oral reference doses of BDE-47, 99, 153, and 209, the mean (95th percentile) of the total hazard quotient was estimated to be 9.1 × 10⁻⁵ (2.7 × 10⁻⁴). Therefore, there is little risk related to BFR exposure for patients taking formulated Pheretima-containing TCMs. However, it is necessary to establish routine monitoring programs for the co-existence of pollutants in TCMs to perform a systematic and comprehensive risk assessment.