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.

It is widely recognized that green infrastructures in urban ecosystems provides important ecosystem services, including air purification. The potential absorption of nitrogen oxides (NOₓ) by urban trees has not been fully quantified, although it is important for air pollution mitigation and the well-being of urban residents. In this study, four common tree species (Sophora japonica L., Fraxinus chinensis Roxb., Populus tomentosa Carrière, Sabina chinensis (L.)) in Beijing, China, were studied. The dual stable isotopes (¹⁵N and ¹⁸O) and a Bayesian isotope mixing model were applied to estimate the sources contributions of potential nitrogen sources to the roadside trees based on leaf and soil sampling in urban regions. The following order of sources contributions was determined: soil > dry deposition > traffic-related NOₓ. The capacity of urban trees for NOₓ removal in the city was estimated using a remote sensing and GIS approach, and the removal capacity was found to range from 0.79 to 1.11 g m⁻² a⁻¹ across administrative regions, indicating that 1304 tons of NOₓ could be potentially removed by urban trees in 2019. Our finding qualified the potential NOₓ removal by urban trees in terms of atmospheric pollution mitigation, highlighting the role of green infrastructure in air purification, which should be taken into account by stakeholders to manage green infrastructure as the basis of a nature-based approach.

Tropospheric ozone is a dangerous atmospheric pollutant for forest ecosystems when it penetrates stomata. Thresholds for ozone-risk assessment are based on accumulated stomatal ozone fluxes such as the Phytotoxic Ozone Dose (POD). In order to identify the effect of ozone on a Holm oak forest in central Italy, four flux-based ozone impact response functions were implemented and tested in a multi-layer canopy model AIRTREE and evaluated against Gross Primary Productivity (GPP) obtained from observations of Eddy Covariance fluxes of CO₂. To evaluate if a clear phytotoxic threshold exists and if it changes during the year, six different detoxifying thresholds ranging between 0 and 5 nmol O₃ m⁻² s⁻¹ were tested.The use of species-specific rather than more general response functions based on plant functional types (PFT) increased model accuracy (RMSE reduced by up to 8.5%). In the case of linear response functions, a threshold of 1 nmol m⁻² s⁻² produced the best results for simulations of the whole year, although the tolerance to ozone changed seasonally, with higher tolerance (5 nmol m⁻² s⁻¹ or no ozone impact) for Winter and Spring and lower thresholds in Summer and Fall (0–1 nmol m⁻² s⁻¹). A “dynamic threshold” obtained by extracting the best daily threshold values from a range of different simulations helped reduce model overestimation of GPP by 213 g C m⁻² y⁻¹ and reduce RMSE up to 7.7%. Finally, a nonlinear ozone correction based on manipulative experiments produced the best results when no detoxifying threshold was applied (0 nmol O₃ m⁻² s⁻¹), suggesting that nonlinear functions fully account for ozone detoxification. The evidence of seasonal changes in ozone tolerance points to the need for seasonal thresholds to predict ozone damage and highlights the importance of performing more species-specific manipulative experiments to derive response functions for a broad range of plant species.

Based on the wind erosion equation and the use of moderate resolution imaging spectroradiometer (MODIS) satellite remote sensing data combined with parameter normalization processing, an optimized high spatial-temporal resolution soil fugitive dust (SFD) emission inventory compiling method was proposed in this study. The "2 + 26" cities in northern China, where heavy pollution frequently occurs, were used as a case study. Using the optimized method, we estimated that the PM₅₀, PM₁₀, and PM₂.₅ emissions from SFD of "2 + 26" cities in 2018 were 2,014,927, 1,007,463, and 151,120 tons, respectively. The dust emissions and emission factors of each city presented significant differences and were generally of a greater level in high-latitude areas (such as cities in Hebei Province) than in low-latitude areas (such as cities in Henan and Shandong Province). Moreover, with an increase in latitude, vegetation cover factors generally exhibit an upward trend, while temperature and rainfall exhibit a downward trend. The dust emissions in the different months showed significant differences. The total dust emission reached the highest level in "late winter–early spring" season (February to April), and the monthly emission accounted for 15–17% of the annual emissions. While in the "summer–autumn" season (July to November), it is the lowest level of the whole year, monthly emissions accounted for 3–5% of the annual emissions. The emission inventory method proposed in this study can provide a reference for dust emission assessment and further pollution prevention and control work.

High concentrations of ground-level ozone affect human health, plants, and animals. Reducing ozone pollution in rural regions, where local emissions are already low, poses challenge. We use meteorological back-trajectories, air quality model sensitivity analysis, and satellite remote sensing data to investigate the ozone sources in Yuma, Arizona and find strong international influences from Northern Mexico on 12 out of 16 ozone exceedance days. We find that such exceedances could not be mitigated by reducing emissions in Arizona; complete removal of state emissions would reduce the maximum daily 8-h average (MDA8) ozone in Yuma by only 0.7% on exceeding days. In contrast, emissions in Mexico are estimated to contribute to 11% of the ozone during these exceedances, and their reduction would reduce MDA8 ozone in Yuma to below the standard. Using satellite-based remote sensing measurements, we find that emissions of nitrogen oxides (NOₓ, a key photochemical precursor of ozone) increase slightly in Mexico from 2005 to 2016, opposite to decreases shown in the bottom-up inventory. In comparison, a decrease of NOₓ emissions in the US and meteorological factors lead to an overall of summer mean and annual MDA8 ozone in Yuma (by ∼1–4% and ∼3%, respectively). Analysis of meteorological back-trajectories also shows similar transboundary transport of ozone at the US-Mexico border in California and New Mexico, where strong influences from Northern Mexico coincide with 11 out of 17 and 6 out of 8 ozone exceedances. 2020 is the final year of the U.S.-Mexico Border 2020 Program, which aimed to reduce pollution at border regions of the US and Mexico. Our results indicate the importance of sustaining a substantial cooperative program to improve air quality at the border area.

Environmental quality data sets are typically imbalanced, because environmental pollution events are rarely observed in daily life. Prediction of imbalanced data sets is a major challenge in machine learning. Our recent work has shown deep cascade forest (DCF), as a base learning model, is promising to be recommended for environmental quality prediction. Although some traditional models were improved by introducing the cost matrix, little is known about whether cost matrix could enhance the prediction performance of DCF. Additionally, feature extraction is also an important way to potentially improve the model's ability to predict the imbalanced data. Here, we developed two novelty learning models based on DCF: cost-sensitive DCF (CS-DCF) and DCF that combines unsupervised learning models and greedy methods (USM-DCF-G). Subsequently, CS-DCF and USM-DCF-G were successfully verified by an imbalanced drinking water quality data set. Our data presented both CS-DCF and USM-DCF-G show better prediction performance than that of DCF alone did. In particular, USM-DCF-G shows the best performance with the highest F1-score (95.12 ± 2.56%), after feature extraction and selection by using unsupervised learning models and greedy methods. Thus, the two learning models, especially USM-DCF-G, were promising learning models to address environmental imbalanced issues and accurately predict environmental quality.

Phosphorus (P) cycling present in sediments associated with iron (Fe), manganese (Mn) and sulfur (S) geochemical processes may cause secondary pollution in overlying water. Understanding the mechanisms of P release from sediments should help to restore water quality. This study used the diffusive gradients in thin film (DGT) technique to investigate the seasonal variation in the lability, remobilization mechanisms, and release characteristics of sediment P in the uncontaminated Xizhi River and the severely contaminated Danshui River, South China. P accumulation in sediments contributed to higher DGT-labile P concentrations in contaminated reaches, and the highest labile P concentrations were generally observed in summer season at each site. The significant positive relationships (p < 0.05) between labile Fe and P confirmed the Fe–P coupling release mechanism in uncontaminated sediments. Stronger relationships between labile Mn and P at contaminated sites indicated that Mn oxides played an important role in P remobilization. However, sulfate reduction associated with microbial activities (crucial genera: Desulfobulbus, Desulfomicrobium and Desulforhabdus) was considered to decouple the Fe & Mn–P cycling relationship, promoting P release at contaminated sites. The effluxes of sediment P were much higher in the Danshui River (mean 0.132 mg cm⁻²·d⁻¹) than in the Xizhi River (mean 0.038 mg cm⁻²·d⁻¹). And hot season led to growth in P effluxes that was much greater in contaminated river.

The combined use of nano-TiO₂ with cementitious materials offers an environmentally-friendly way to combat the air pollution problem. However, a trade-off between a high efficiency and a robust weathering resistance has often to be made for most of the attempted nano-TiO₂ incorporation methods. This paper developed a simple and effective “spraying” method to coat nano-TiO₂ particles on the surface of concrete surface layers (CSL). The results showed that the NOₓ removal rate of the samples increased with an increase in both the concentrations of nano-TiO₂ solutions and the number of times of the spraying action. And the conditions for preparation of the Spray AB (the CSL were first sprayed with the 30 g L⁻¹ TiO₂-solution 20 times, followed by mechanical compaction, and for another 20 times after the compaction) were found to be optimal in terms of NOₓ removal performance and weathering resistance. The Spray AB was superior to the 5% TiO₂-intermixed samples with respect to photocatalytic NOₓ removal ability. Compared with TiO₂-dip-coated samples, the Spray AB samples had better and robust weathering resistance. A case study on the factory-fabricated green Eco-blocks (produced by the laboratory-developed spray method and the conventional intermix method) was performed. Examination and comparison on their respective photocatalytic NOₓ removal further verified the advantages of the spray method over the intermix method.

With numerous new chemicals introduced into the environment everyday, identification of their potential hazards to the environment and human health is a considerable challenge. Developing adverse outcome pathway (AOP) framework is promising in helping to achieve this goal as it can bring In Vitro testing into toxicity measurement and understanding. To explore the toxic mechanism underlying environmental chemicals via the AOP approach, an integration of adequate experimental data with systems biology understanding is preferred. Here, we describe a novel method to develop reliable and sensible AOPs that relies on chemical-gene interactions, toxicity pathways, molecular regulations, phenotypes, and outcomes information obtained from comparative toxicogenomics database (CTD) and Ingenuity Pathway Analysis (IPA). Using Benzo(a)pyrene (BaP), a highly studied chemical as a stressor, we identified the pivotal IPA toxicity pathways, the molecular initiating event (MIE), and candidate key events (KEs) to structure AOPs in the liver and lung, respectively. Further, we used the corresponding CTD information of multiple typical AHR-ligands, including 2,3,7,8-tetrachlorodibenzoparadioxin (TCDD), valproic acid, quercetin, and particulate matter, to validate our AOP networks. Our approach is likely to speed up AOP development as providing a time- and cost-efficient way to collect all fragmented bioinformation in published studies. It also facilitates a better understanding of the toxic mechanism of environmental chemicals, and potentially brings new insights into the screening of critical paths in the AOP network.

Existing mutagenicity tests for metazoans lack the direct observation of enhanced germline mutation rates after exposure to anthropogenic substances, therefore being inefficient. Cadmium (Cd) is a metal described as a mutagen in mammalian cells and listed as a group 1 carcinogenic and mutagenic substance. But Cd mutagenesis mechanism is not yet clear. Therefore, in the present study, we propose a method coupling short-term mutation accumulation (MA) lines with subsequent whole genome sequencing (WGS) and a dedicated data analysis pipeline to investigate if chronic Cd exposure on Chironomus riparius can alter the rate at which de novo point mutations appear. Results show that Cd exposure did not affect the basal germline mutation rate nor the mutational spectrum in C. riparius, thereby arguing that exposed organisms might experience a range of other toxic effects before any mutagenic effect may occur. We show that it is possible to establish a practical and easily implemented pipeline to rapidly detect germ cell mutagens in a metazoan test organism. Furthermore, our data implicate that it is questionable to transfer mutagenicity assessments based on in vitro methods to complex metazoans.