In recent decades, industrial emissions have been reduced in many countries, which provides an opportunity for the recovery of polluted ecosystems. However, our knowledge of the rate and factors facilitating the recovery of local bird populations after pollution abatement is incomplete. Long-term (1989–2021) annual observations on nest-box populations of a passerine bird, Ficedula hypoleuca, were used to analyze temporal dynamics of breeding parameters following a 50-fold reduction of industrial emissions from the Middle Ural copper smelter (MUCS) according to pollution zone, habitat, air temperature, and breeding density. In the heavily polluted (impact) zone (1–2 km of MUCS), egg and fledgling production were strongly impaired compared to the moderately polluted (buffer zone, 4–8 km of MUCS) and unpolluted control zone (16–27 km of MUCS). During the study period, the laying date advanced along with increasing spring air temperatures. The clutch size increased in the impact zone by 26%, in the buffer zone by 10%, and in control by 5%. The number of fledglings increased in the impact zone by 102% and the buffer zone by 17%. In the most recent year (2021), mean laying date, clutch size, fledgling production, and the frequency of nests with unhatched eggs in the impact zone did not reach the control level, whereas the frequency of nests with perished chicks did not differ among zones. Breeding parameters of birds in the impact zone improved slowly, likely due to the slow recovery of habitats. We conclude that bird reproduction may require many decades to recover fully in the heavily polluted zone.

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.

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.

Forest understory plant communities in the eastern United States are often diverse and are potentially sensitive to changes in climate and atmospheric inputs of nitrogen caused by air pollution. In recent years, empirical and processed-based mathematical models have been developed to investigate such changes in plant communities. In the study reported here, a robust set of understory vegetation response functions (expressed as version 2 of the Probability of Occurrence of Plant Species model for the United States [US-PROPS v2]) was developed based on observations of forest understory and grassland plant species presence/absence and associated abiotic characteristics derived from spatial datasets. Improvements to the US-PROPS model, relative to version 1, were mostly focused on inclusion of additional input data, development of custom species-level input datasets, and implementation of methods to address uncertainty. We investigated the application of US-PROPS v2 to evaluate the potential impacts of atmospheric nitrogen (N) and sulfur (S) deposition, and climate change on forest ecosystems at three forested sites located in New Hampshire, Virginia, and Tennessee in the eastern United States. Species-level N and S critical loads (CLs) were determined under ambient deposition at all three modeled sites. The lowest species-level CLs of N deposition at each site were between 2 and 11 kg N/ha/yr. Similarly, the lowest CLs of S deposition, based on the predicted soil pH response, were less than 2 kg S/ha/yr among the three sites. Critical load exceedance was found at all three model sites. The New Hampshire site included the largest percentage of species in exceedance. Simulated warming air temperature typically resulted in lower maximum occurrence probability, which contributed to lower CLs of N and S deposition. The US-PROPS v2 model, together with the PROPS-CLF model to derive CL functions, can be used to develop site-specific CLs for understory plants within broad regions of the United States. This study demonstrates that species-level CLs of N and S deposition are spatially variable according to the climate, light availability, and soil characteristics at a given location. Although the species niche models generally performed well in predicting occurrence probability, there remains uncertainty with respect to the accuracy of reported CLs. As such, the specific CLs reported here should be considered as preliminary estimates.

There have been few large multicity studies to evaluate the acute health effects of ambient air pollution on morbidity risk in developing counties. In this study, we examined the short-term associations of air pollution with daily hospital admissions in China. We conducted a nationwide time-series study in 218 Chinese cities between 2014 and 2016. Data on daily hospital admissions counts were obtained from the National Health Insurance Database for Urban Employees covering 0.28 billion enrollees. We used generalized additive model with Poisson regression to estimate the associations in each city, and we performed random-effects meta-analysis to pool the city-specific estimates. More than 60 million hospital admissions were analyzed in this study. At the national-average level, each 10 μg/m³ increase in PM₁₀, SO₂, and NO₂, and 1 mg/m³ increase in CO at lag 0 day was associated with a 0.29% (95% CI, 0.23%–0.36%), 1.16% (95% CI, 0.92%–1.40%), 1.68% (95% CI, 1.40%–1.95%), and 2.59% (95% CI, 1.69%–3.50%) higher daily hospital admissions, respectively. The associations of air pollution with hospital admissions remained statistically significant at levels below the current Chinese Ambient Air Quality Standards. The effect estimates were larger in cities with lower air pollutants levels or higher air temperatures and relative humidity, as well as in the elderly. In conclusion, our findings provide robust evidence of increased hospital admissions in association with short-term exposure to ambient air pollution in China.

Multi-stage constructed wetlands (CWs) have been proved to be a cost-effective alternative in the treatment of various wastewaters for improving the treatment performance as compared with the conventional single-stage CWs. However, few long-term full-scale multi-stage CWs have been performed and evaluated for polishing effluents from domestic wastewater treatment plants (WWTP). This study investigated the seasonal and spatial dynamics of carbon and the effects of the key factors (input loading and temperature) in the large-scale seven-stage Wu River CW polishing domestic WWTP effluents in northern China. The results indicated a significant improvement in water quality. Significant seasonal and spatial variations of organics removal were observed in the Wu River CW with a higher COD removal efficiency of 64–66% in summer and fall. Obvious seasonal and spatial variations of CH₄ and CO₂ emissions were also found with the average CH₄ and CO₂ emission rates of 3.78–35.54 mg m⁻² d⁻¹ and 610.78–8992.71 mg m⁻² d⁻¹, respectively, while the higher CH₄ and CO₂ emission flux was obtained in spring and summer. Seasonal air temperatures and inflow COD loading rates significantly affected organics removal and CH₄ emission, but they appeared to have a weak influence on CO₂ emission. Overall, this study suggested that large-scale Wu River CW might be a potential source of GHG, but considering the sustainability of the multi-stage CW, the inflow COD loading rate of 1.8–2.0 g m⁻² d⁻¹ and temperature of 15–20 °C may be the suitable condition for achieving the higher organics removal efficiency and lower greenhouse gases (GHG) emission in polishing the domestic WWTP effluent. The obtained knowledge of the carbon dynamics in large-scale Wu River CW will be helpful for understanding the carbon cycles, but also can provide useful field experience for the design, operation and management of multi-stage CW treatments.

Mediterranean Basin ecosystems, their unique biodiversity, and the key services they provide are currently at risk due to air pollution and climate change, yet only a limited number of isolated and geographically-restricted studies have addressed this topic, often with contrasting results. Particularities of air pollution in this region include high O3 levels due to high air temperatures and solar radiation, the stability of air masses, and dominance of dry over wet nitrogen deposition. Moreover, the unique abiotic and biotic factors (e.g., climate, vegetation type, relevance of Saharan dust inputs) modulating the response of Mediterranean ecosystems at various spatiotemporal scales make it difficult to understand, and thus predict, the consequences of human activities that cause air pollution in the Mediterranean Basin. Therefore, there is an urgent need to implement coordinated research and experimental platforms along with wider environmental monitoring networks in the region. In particular, a robust deposition monitoring network in conjunction with modelling estimates is crucial, possibly including a set of common biomonitors (ideally cryptogams, an important component of the Mediterranean vegetation), to help refine pollutant deposition maps. Additionally, increased attention must be paid to functional diversity measures in future air pollution and climate change studies to establish the necessary link between biodiversity and the provision of ecosystem services in Mediterranean ecosystems. Through a coordinated effort, the Mediterranean scientific community can fill the above-mentioned gaps and reach a greater understanding of the mechanisms underlying the combined effects of air pollution and climate change in the Mediterranean Basin.

Identifying phosphorus (P) sources, distribution and export from lowland polders is important for P pollution management, however, is challenging due to the high complexity of hydrological and P transport processes in lowland areas. In this study, the spatial pattern and temporal dynamics of P export coefficient (PEC) from all the 2539 polders in Lake Taihu Basin, China were estimated using a coupled P model for describing P dynamics in a polder system. The estimated amount of P export from polders in Lake Taihu Basin during 2013 was 1916.2 t/yr, with a spatially-averaged PEC of 1.8 kg/ha/yr. PEC had peak values (more than 4.0 kg/ha/yr) in the polders near/within the large cities, and was high during the rice-cropping season. Sensitivity analysis based on the coupled P model revealed that the sensitive factors controlling the PEC varied spatially and changed through time. Precipitation and air temperature were the most sensitive factors controlling PEC. Culvert controlling and fertilization were sensitive factors controlling PEC during some periods. This study demonstrated an estimation of PEC from 2539 polders in Lake Taihu Basin, and an identification of sensitive environmental factors affecting PEC. The investigation of polder P export in a watershed scale is helpful for water managers to learn the distribution of P sources, to identify key P sources, and thus to achieve best management practice in controlling P export from lowland areas.

The response speed of ambulance calls is very crucial to rescue patients suffering immediately life threatening conditions. The serious health outcomes might be caused by exposing to extreme heat only several hours before. However, limited evidence is available on this topic. This study aims to examine the hourly association between heat and ambulance calls, to improve the ambulance services and to better protect health.Hourly data on ambulance calls for non-accidental causes, temperature and air pollutants (PM10, NO2, and O3) were collected from Brisbane, Australia, during 2001 and 2007. A time-stratified case-crossover design was used to examine the associations between hourly ambulance calls and temperature during warm season (Nov, Dec, Jan, Feb, and Mar), while adjusting for potential confounders. Stratified analyses were performed for sex and age groups.Ambulance calls peaked at 10am for all groups, except those aged <15 years at 19pm, while temperature was hottest at 13pm. The hourly heat-ambulance calls relationships were non-linear for all groups, with thresholds between 27 °C and 31 °C. The associations appeared immediately, and lasted for about 24 h. There were no significant modification effect by sex and age.The findings suggest that hot hourly temperatures (>27 °C) increase the demands of ambulance. This information is helpful to increase the efficiency of ambulance service then save lives, for example, preparing more ambulance before appearance of extremely hot temperature in combination with weather forecast. Also, people should better arrange their time for outdoor activities to avoid exposing to extreme hot temperatures.