Global warming is drastically altering the climate conditions of our planet. Soils will be among the most affected components of terrestrial ecosystems, especially in contaminated areas. In this study we investigated if changes in climate conditions (air temperature and soil moisture) affect the toxicity of historically metal(loid)-contaminated soils to the invertebrate Folsomia candida, followed by an assessment of its recovery capacity. Ecotoxicity tests (assessing survival, reproduction) were performed in field soils affected by metal(loid)s under different climate scenarios, simulated by individually changing air temperature or soil moisture conditions. The scenarios tested were: standard conditions (20°C + 50% soil water holding capacity-WHC); increased air temperature (daily fluctuation of 20–30°C + 50% WHC); soil drought (20°C + 25% WHC); soil flood (20°C + 75% WHC). Recovery potential was assessed under standard conditions in clean soil. Increased temperature was the major climate condition negatively affecting collembolans performance (decreased survival and reproduction), regardless of metal(loid) contamination. Drought and flood conditions presented less pronounced effects. When it was possible to move to the recovery phase (enough juveniles in exposure phase), F. candida was apparently able to recover from the exposure to metal(loid) contamination and/or climate alterations. The present study showed that forecasted climate alterations in areas already affected by contamination should be considered to improve environmental risk assessment.

Mitigation of ambient ozone (O₃) pollution is a great challenge because it depends heavily on the background O₃ which has been poorly evaluated in many regions, including in China. By establishing the relationship between O₃ and air temperature near the surface, the mean background O₃ mixing ratios in the clean and polluted seasons were determined to be 35–40 and 50–55 ppbv in China during 2013–2019, respectively. Simulations using the chemical transport model (i.e., the Weather Research and Forecasting coupled with Chemistry model, WRF/Chem) suggested that biogenic volatile organic compounds (VOC) emissions were the primary contributor to the increase in the background O₃ in the polluted season (BOP) compared to the background O₃ in the clean season (BOC), ranging from 8 ppbv to 16 ppbv. More importantly, the BOP continuously increased at a rate of 0.6–8.0 ppbv yr⁻¹ during 2013–2019, while the non-BOP stopped increasing after 2017. Consequently, an additional 2%–16% reduction in anthropogenic VOC emissions is required to reverse the current O₃ back to that measured in the period from 2013 to 2017. The results of this study emphasize the importance of the relative contribution of the background O₃ to the observed total O₃ concentration in the design of anthropogenic precursor emission control strategies for the attainment of O₃ standards.

In spring, ozone (O₃) pollution frequently occurrs in eastern China, but key drivers remain uncertain. In this study, interannual variations in springtime ozone in Shanghai, China, from 2013 to 2021, were investigated to assess the health impacts and the effectiveness of recent air pollution control measures. A combination of ground-level measurements of regulated air pollutants, lidar observations, and backward trajectories of air masses was used to identify the key drivers for enhancing springtime O₃. The results show that external imports of O₃ driven by atmospheric circulation are notable sources of springtime surface O₃. For example, the downward transport from the free troposphere could contribute to over 50% of surface O₃ in the morning. The surface O₃ mixing ratios in spring exhibited an upward trend of 0.93 ppb yr⁻¹ (p < 0.05) from 2013 to 2021. The change in meteorological variables, particularly the increase in air temperature, could explain nearly 87% of the springtime O₃ upward trend. The change in anthropogenic emissions of precursors only contributed to a small fraction (<13%) of the increase in springtime O₃. The cumulative exposure of urban residents to O₃ in spring also exhibited a significant upward trend (111 ppb yr⁻¹, p < 0.05). With the rapid increase in surface O₃, premature respiratory mortality attributable to O₃ exposure has fluctuated at approximately 2933 deaths per year since 2016, even though the total deaths from respiratory diseases have significantly declined. Long-term exposure to high O₃ concentrations is a significant contributor to premature respiratory mortality.

This research study uses Artificial Neural Networks (ANNs) to predict occupational accidents in Sivakasi firework industries. Atmospheric temperature, pressure and humidity are the causes of explosion during chemical mixing, drying, and pellet making. The Proposed ANN model predicts the accidents and the session of accidents (FN/AN) based on atmospheric conditions. This prediction takes values from historical accident data due to the atmospheric conditions of Sivakasi (2009–2021). In the development of ANN model, the Feed-Forward Back Propagation (FFBP) with the Levenberg-Marquardt function has been employed with hidden layers of 5 and 10 to train the network. The performance accuracy of both the hidden layers is evaluated and compared with other models like Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (K-NN). The accuracy of the proposed model for accident classification is 82.7% and 67.8% for hidden layers 5 and 10, respectively. Also, the model predicts the session of accident with the accuracy of 72% and 54%, specificity of 77.7% and 60.1%, sensitivity of 69% and 52.92% for hidden layers 5 and 10, respectively. It is found that hidden layer 5 gives higher accuracy than hidden layer 10. The proposed ANN model gives the highest accuracy when compared to other models. This study is helpful in the firework industry management, and workers improve safety precautions and avoid explosions due to atmospheric conditions.

Nitrogen (N) is an essential nutrient but may become a pollution source in the environment when the N concentration exceeds a certain threshold for humans and nature. Nitrate is a major N species in river water with notable spatial and temporal variations under the influences of natural factors and anthropogenic N inputs. We analyzed the relationship between riverine N (focusing on nitrate) concentration and various factors (land use, climate, basin topography, atmospheric N deposition, agricultural N sources and human-derived N) in 104 rivers located throughout the Japanese Archipelago except small remote islands. We aimed to better understand processes and mechanisms to explain the spatial and temporal changes in riverine nitrate concentration. A publicly available river water quality database observed in the 1980s (1980–1989) and 2000s (2000–2009) was used. This study is the first to evaluate the long-term scale of 20 years in the latter half of Japan's economic growth period at the national level. A geographic information system (GIS) was employed to determine average values of each variable collected from multiple sources of statistical data. We then performed regression analysis and structural equation modeling (SEM) for each period. The forestland area influenced by the basin topography, climate (i.e., air temperature) and other land uses (i.e., farmland and urban area) played a major role in decreasing nitrate concentrations in both the 1980s and 2000s. Atmospheric N deposition (especially N oxides) and agricultural N sources (fertilizer and manure) were also significant variables regarding the spatial variations in riverine nitrate concentrations. The SEM results suggested that human-derived N (via food consumption) intensified by demographic shifts during the 2000s increased riverine nitrate concentrations over other variables within the context of spatial variation. These findings facilitate better decision making regarding land use, agricultural practices, pollution control and individual behaviors toward a sustainable society.

Antarctic trace element records could provide important insights into the impact of human activities on the environment over the past few centuries. In this study, we investigated the atmospheric concentrations of 14 representative heavy metals (Al, As, Cd, Co, Cu, Fe, K, Mg, Mn, Pb, Sb, Sr, Tl and V) from 174 samples collected in a 4-m snow pit at Dome Argus (Dome A) on the East Antarctic Plateau, covering the period from 1950 to 2016 A.D. We found great variability in the annual concentration of all metals. The crustal enrichment factors suggest that the concentrations of some heavy metals (Cd, Sb, Cu, As and Pb) were likely influenced by anthropogenic activities in recent decades. An analysis of source regions suggests that heavy metal pollution at Dome A was largely caused by human activities in Australia and South America (e.g. mining production, leaded gasoline). Based on the relationship between the trace elements fluxes and sea ice concentration (SIC), sea surface temperature (SST) and annual mean air temperature at 2 m above the ground (T₂ₘ), our analysis shows that deposition and transport of atmospheric aerosol at Dome A were influenced by circum-Antarctic atmospheric circulations.

The increasing demand for fresh water coupled with the need to recycle water and nutrients has witnessed a global increase in wastewater irrigation. However, the development of antibiotic resistance hotspots in different environmental compartments, as a result of wastewater reuse is becoming a global health concern. The effect of irrigation water sources (wastewater, surface water, fresh water) on the presence and abundance of antibiotic resistance genes (ARGs) (blaCTX₋ₘ₋₃₂, tet-W, sul1, cml-A, and erm-B) and class 1 integrons (intI1) were investigated in the irrigation water-soil-crop continuum using quantitative real-time PCR (qPCR). Sul1 and blaCTX₋ₘ₋₃₂ were the most and least abundant ARGs in three environments, respectively. The abundance of ARGs and intI1 significantly decreased from wastewater to surface water and then fresh water. However, irrigation water sources had no significant effect on the abundance of ARGs and intI1 in soil and crop samples. Principal component analysis (PCA) showed that UV index and air temperature attenuate the abundance of ARGs and intI1 in crop samples whereas the air humidity and soil electrical conductivity (EC) promotes the ARGs and intI1. So that the climate condition of semi-arid regions significantly affects the abundance of ARGs and intI1 in crop samples. The results suggest that treated wastewater might be safely reused in agricultural practice in semi-arid regions without a significant increase of potential health risks associated with ARGs transfer to the food chain. However, further research is needed for understanding and managing ARGs transfer from the agricultural ecosystem to humans through the food chain.

Dissolved oxygen (DO) is an effective indicator for water pollution. However, since DO is a non-optically active parameter and has little impact on the spectrum captured by satellite sensors, research on estimating DO by remote sensing at multiple spatiotemporal scales is limited. In this study, the support vector regression (SVR) models were developed and validated using the remote sensing reflectance derived from both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and synchronous DO measurements (N = 188) and water temperature of Lake Huron and three other inland waterbodies (N = 282) covering latitude between 22–45 °N. Using the developed models, spatial distributions of the annual and monthly DO variability since 1984 and the annual monthly DO variability since 2000 in Lake Huron were reconstructed for the first time. The impacts of five climate factors on long-term DO trends were analyzed. Results showed that the developed SVR-based models had good robustness and generalization (average R² = 0.91, root mean square percentage error = 2.65%, mean absolute percentage error = 4.21%), and performed better than random forest and multiple linear regression. The monthly DO estimates by Landsat and MODIS data were highly consistent (average R² = 0.88). From 1984 to 2019, the oxygen loss in Lake Huron was 6.56%. Air temperature, incident shortwave radiation flux density, and precipitation were the main climate factors affecting annual DO of Lake Huron. This study demonstrated that using SVR-based models, Landsat and MODIS data could be used for long-term DO retrieval at multiple spatial and temporal scales. As data-driven models, combining spectrum and water temperature as well as extending the training set to cover more DO conditions could effectively improve model robustness and generalization.

Monitoring of surface ozone (O₃) and Nitrogen Oxides (NOx) are vital for understanding the variation and exposure impact of these trace gases over the habitat. The present study analyses the in situ observations of surface O₃ and NOx for January–December 2016, for the first time over three sites of North-Eastern India (Aizwal, Gauhati and Tezpur). The sites are major cities of north-eastern India, located in the foothills of Eastern Himalaya and have no industrial impacts. We have analysed the seasonal variation of O₃ and NOx and found that the site Tezpur, which is in the valley area of Eastern Himalaya, is experiencing higher values of pollutants persisting for a long time compared to the other two stations. The correlation of surface O₃ with the air temperature at all three sites suggested that all the O₃ may not be locally produced, but has the contribution of transported pollution reaching to stations. The study also attempts to discover the existing variability in the surface O₃ and NOx over the study area by employing continuous wavelet analysis.

Burial in sediments is a crucial way to reduce mobilization and risks of hydrophobic organic contaminants (HOCs), but ability of sediments to bury HOCs may be altered if the environment is changed. Whether the ability of sediments to bury HOCs has been affected by climate change remains largely unclear. We excluded the impacts of anthropogenic emissions and eutrophication from that of climate change, and for the first time found that not only the rising surface air temperature but also the declining wind speed and the reducing days with precipitation had weakened the ability of Chinese lakes to bury 16 polycyclic aromatic hydrocarbon (PAHs) by 69.2% ± 9.4%–85.7% ± 3.6% from 1951 to 2017. The relative contributions of the climatic variables to the reduced burial ability depended on the properties of the PAHs, and lakes. Burial ability of the PAHs responded differently to climate change, and was correlated to their volatilization and aqueous solubility, and lake area, catchment area/lake area ratio, and water depth. Our study suggests that not only the rising surface air temperature but also the declining wind speed and the reducing days with precipitation can undermine global efforts to reduce environmental and human exposure to PAHs.