Urban Heat Island (UHI) is posing a significant challenge due to growing urbanisations across the world. Green infrastructure (GI) is popularly used for mitigating the impact of UHI, but knowledge on their optimal use is yet evolving. The UHI effect for large cities have received substantial attention previously. However, the corresponding effect is mostly unknown for towns, where appreciable parts of the population live, in Europe and elsewhere. Therefore, we analysed the possible impact of three vegetation types on UHI under numerous scenarios: baseline/current GI cover (BGI); hypothetical scenario without GI cover (HGI-No); three alternative hypothetical scenarios considering maximum green roofs (HGR-Max), grasslands (HG-Max) and trees (HT-Max) using a dispersion model ADMS-Temperature and Humidity model (ADMS-TH), taking a UK town (Guildford) as a case study area. Differences in an ambient temperature between three different landforms (central urban area, an urban park, and suburban residential area) were also explored. Under all scenarios, the night-time (0200 h; local time) showed a higher temperature increase, up to 1.315 °C due to the lowest atmospheric temperature. The highest average temperature perturbation (change in ambient temperature) was 0.563 °C under HGI-No scenario, followed by HG-Max (0.400 °C), BGI (0.343 °C), HGR-Max (0.326 °C) and HT-Max (0.277 °C). Furthermore, the central urban area experienced a 0.371 °C and 0.401 °C higher ambient temperature compared with its nearby suburban residential area and urban park, respectively. The results allow to conclude that temperature perturbations in urban environments are highly dependent on the type of GI, anthropogenic heat sources (buildings and vehicles) and the percentage of land covered by GI. Among all other forms of GI, trees were the best-suited GI which can play a viable role in reducing the UHI. Green roofs can act as an additional mitigation measure for the reduction of UHI at city scale if large areas are covered.

Field application of liquid animal manure (slurry) is a significant source of ammonia (NH₃) emission to the atmosphere. It is well supported by theory and previous studies that air temperature effects NH₃ flux from field applied slurry. The objectives of this study was to statistically model the response of temperature at the time of application on cumulative NH₃ emission. Data from 19 experiments measured with the same system of dynamic chambers and online measurements were included. A generalized additive model allowing to represent non-linear functional dependences of the emission on the temperature revealed that a positive response of the cumulative NH₃ emission on the temperature at the time of application up to a temperature of approximately 14 °C. Above that, the temperature effect is insignificant. Average temperature over the measuring period was not found to carry any additional information on the cumulative NH₃ emission. The lack of emission response on temperature above a certain point is assumed to be caused by drying out of the slurry and possible crust formation. This effect is hypothesized to create a physical barrier that reduce diffusion of NH₃ to the soil surface, thereby lowering the emission rate. Furthermore, the effect of the interaction between soil type and application technique and the effect of dry matter content of the slurry was derived from the model, and found to be significant on cumulative NH₃ emission predictions.

Atmospheric warming and increasing tropospheric ozone (O₃) concentrations often co-occur in many cities of the world including China, adversely affecting the health status of urban trees. However, little information is known about the combined and interactive effects from increased air temperature (IT) and elevated O₃ (EO) exposures on urban tree species. Here, Ginkgo biloba and Populus alba ‘Berolinensis’ seedlings were subjected to IT (+2 °C of ambient air temperature) and/or EO (+2-fold ambient air O₃ concentrations) for one growing season by using open-top chambers. IT alone had no significant effect on physiological metabolisms at the early growing stage, but significantly increased photosynthetic parameters, antioxidative enzyme activities (P < 0.05). EO alone decreased physiological parameters except for increased oxidative stress. Compared to EO exposure alone, plants grown under IT and EO combined showed higher antioxidative and photosynthetic activity. There was a significant interactive effect between IT and EO on net photosynthetic rate, stomatal conductance, water use efficiency, the maximum quantum efficiency of PSII photochemistry, the actual quantum efficiency of PSII, enzyme activities, aboveground biomass and root/shoot ratio (P < 0.05), respectively. These results suggested that during one growing season, IT mitigated the adverse effect of EO on the tested plants. In addition, we found that G. biloba was more sensitive than P. alba ‘Berolinensis’ to both IT and EO, suggesting that G. biloba may be a good indicator species for climate warming and air pollution, particularly under environmental conditions as they co-occur in urban areas.

As a volatile organic compound existing in the atmosphere, methanol plays a key role in atmospheric chemistry due to its comparatively high abundance and long lifetime. Croplands are a significant source of biogenic methanol, but there is a lack of systematic assessment for the production and emission of methanol from crops in various phases. In this study, methanol emissions from spring wheat during the growing period were estimated using a developed emission model. The temporal and spatial variations of methanol emissions of spring wheat in a Canadian province were investigated. The averaged methanol emission of spring wheat is found to be 37.94 ± 7.5 μg·m⁻²·h⁻¹, increasing from north to south and exhibiting phenological peak to valley characteristics. Moreover, cold crop districts are projected to be with higher increase in air temperature and consequent methanol emissions during 2020–2099. Furthermore, the seasonality of methanol emissions is found to be positively correlated to concentrations of CO, filterable particulate matter, and PM₁₀ but negatively related to NO₂ and O₃. The uncertainty and sensitivity analysis results suggest that methanol emissions show a Gamma probabilistic distribution, and growth length, air temperature, solar radiation and leafage are the most important influencing variables. In most cases, methanol emissions increase with air temperature in the range of 3–35 °C while the excessive temperature may result in decreased methanol emissions because of inactivated enzyme activity or increased instant methanol emissions due to heat injury. Notably, induced emission might be the major source of biogenic methanol of mature leaves. The results of this study can be used to develop appropriate strategies for regional emission management of cropping systems.

The performance of the radon (²²²Rn)-deficit technique has been evaluated at a site in which a complex DNAPL mixture (mostly hexachlorocyclohexanes and chlorobenzenes) has contaminated all four layers (from top to bottom: anthropic backfill, silt, gravel and marl) of the soil profile. Soil gas samples were collected at two depths (0.8 m and 1.7 m) in seven field campaigns and a total of 186 ²²²Rn measurements were performed with a pulse ionization detector. A statistical assessment of the influence of field parameters on the results revealed that sampling depth and atmospheric pressure did not significantly affect the measurements, while the location of the sampling point and ground-level atmospheric temperature did. In order to remove the bias introduced by varying field temperatures and hence to be able to jointly interpret ²²²Rn measurements from different campaigns, ²²²Rn concentrations were rescaled by dividing each individual datum by the mean ²²²Rn concentration of its corresponding field campaign. Rescaled ²²²Rn maps showed a high spatial correlation between ²²²Rn minima and maximum contaminant concentrations in the top two layers of the soil profile, successfully delineating the surface trace of DNAPL accumulation in the anthropic backfill and silt layers. However, no correlation could be established between ²²²Rn concentrations in superficial soil gas and contaminant concentration in the deeper two layers of the soil profile. These results indicate that the ²²²Rn-deficit technique is unable to describe the vertical variation of contamination processes with depth but can be an effective tool for the preliminary characterization of sites in which the distance between the inlet point of the sampling probe and the contaminant accumulation falls within the effective diffusion length of ²²²Rn in the affected soil profile.

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.

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 use of agro-biowaste compost fertilizers in agriculture is beneficial from technical, financial, and environmental perspectives. Nevertheless, the physical, mechanical, and agronomical attributes of agro-biowaste compost fertilizers should be engineered to reduce their storage, handling, and utilization costs and environmental impacts. Pelletizing and drying are promising techniques to achieve these goals. In the present work, the effects of process parameters, including compost particle size/moisture content, pelletizing compression ratio, and drying air temperature/velocity, were investigated on the density, specific crushing energy, and moisture diffusion of agro-biowaste compost pellet. The Taguchi technique was applied to understand the effects of independent parameters on the output responses, while the optimal pellet properties were found using the iterative thresholding method. The soil and plant (sweet basil) response to the optimal biocompost pellet was experimentally evaluated. The farm application of the optimal pellet was also compared with the untreated agro-biowaste compost using the life cycle assessment approach to investigate the potential environmental impact mitigation of the pelletizing and drying processes. Generally, the compost moisture content was the most influential factor on the density and specific crushing energy of the dried pellet, while the moisture diffusion of the wet pellet during the drying process was significantly influenced by the pelletizing compression ratio. The density, specific crushing energy, and moisture diffusion of agro-biowaste compost pellet at the optimal conditions were 1242.49 kg/m³, 0.5054 MJ/t, and 8.2 × 10⁻⁸ m²/s, respectively. The optimal biocompost pellet could release 80% of its nitrogen content evenly over 98 days, while this value was 28 days for the chemical urea fertilizer. Besides, the optimal pellet could significantly improve the agronomical attributes of the sweet basil plant compared with the untreated biocompost. The applied strategy could collectively mitigate the weighted environmental impact of farm application of the agro-biowaste compost by more than 63%. This reduction could be attributed to the fact that the pelletizing-drying processes could avoid methane emissions from the untreated agro-biowaste compost during the farm application. Overall, pelletizing-drying of the agro-biowaste compost could be regarded as a promising strategy to improve the environmental and agronomical performance of farm application of organic biofertilizers.

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.