Antimicrobial nanomaterials provide numerous opportunities for the synthesis of next-generation sustainable water disinfectants. Using the keywords graphene and water disinfection and graphene antibacterial activity, a detailed search of the Scopus database yielded 198 and 1433 studies on using graphene for water disinfection applications and graphene antibacterial activity in the last ten years, respectively. Graphene family nanomaterials (GFNs) have emerged as effective antibacterial agents. The current innovations in graphene-, graphene oxide (GO)-, reduced graphene oxide (rGO)-, and graphene quantum dot (GQD)-based nanocomposites for water disinfection, including their functionalization with semiconductor photocatalysts and metal and metal oxide nanoparticles, have been thoroughly discussed in this review. Furthermore, their novel application in the fabrication of 3D porous hydrogels, thin films, and membranes has been emphasized. The physicochemical and structural properties affecting their antibacterial efficiency, such as sheet size, layer number, shape, edges, smoothness/roughness, arrangement mode, aggregation, dispersibility, and surface functionalization have been highlighted. The various mechanisms involved in GFN antibacterial action have been reviewed, including the mechanisms of membrane stress, ROS-dependent and -independent oxidative stress, cell wrapping/trapping, charge transfer, and interaction with cellular components. For safe applications, the potential biosafety and biocompatibility of GFNs in aquatic environments are emphasized. Finally, the current limitations and future perspectives are discussed. This review may provide ideas for developing efficient and practical solutions using graphene-, GO-, rGO-, and GQD-based nanocomposites in water disinfection by rationally employing their unique properties.

Airborne bacteria may absorb the substance from the atmospheric particles and play a role in biogeochemical cycling. However, these studies focused on a few culturable bacteria and the samples were usually collected from one site. The metabolic potential of a majority of airborne bacteria on a regional scale and their driving factors remain unknown. In this study, we collected particulates with aerodynamic diameter ≤2.5 μm (PM₂.₅) from 8 cities that represent different regions across China and analyzed the samples via high-throughput sequencing of 16S rRNA genes, quantitative polymerase chain reaction (qPCR) analysis, and functional database prediction. Based on the FAPROTAX database, 326 (80.69%), 191 (47.28%) and 45 (11.14%) bacterial genera are possible to conduct the pathways of carbon, nitrogen, and sulfur cycles, respectively. The pathway analysis indicated that airborne bacteria may lead to the decrease in organic carbon while the increase in ammonium and sulfate in PM₂.₅ samples, all of which are the important components of PM₂.₅. Among the 19 environmental factors studied including air pollutants, meteorological factors, and geographical conditions, PM₂.₅ concentration manifested the strongest correlations with the functional genes for the transformation of ammonium and sulfate. Moreover, the PM₂.₅ concentration rather than the sampling site will drive the distribution of functional genera. Thus, a bi-directional relationship between PM₂.₅ and bacterial metabolism is suggested. Our findings shed light on the potential bacterial pathway for the biogeochemical cycling in the atmosphere and the important role of PM₂.₅, offering a new perspective for atmospheric ecology and pollution control.

Cyclists can be easily exposed to traffic-related pollutants due to riding on or close to the road during commuting in cities. PM₂.₅ has been identified as one of the major pollutants emitted by vehicles and associated with cardiopulmonary and respiratory diseases. As routing has been suggested to reduce the exposures for cyclists, in this study, PM₂.₅ was monitored with low-cost sensors during commuting periods to develop models for identifying low exposure routes in three Asian cities: Taipei, Osaka, and Seoul. The models for mapping the PM₂.₅ in the cities were developed by employing the random forest algorithm in a two-stage modeling approach. The land use features to explain spatial variation of PM₂.₅ were obtained from the open-source land use database, OpenStreetMap. The total length of the monitoring routes ranged from 101.36 to 148.22 km and the average PM₂.₅ ranged from 13.51 to 15.40 μg/m³ among the cities. The two-stage models had the standard k-fold cross-validation (CV) R² of 0.93, 0.74, and 0.84 in Taipei, Osaka, and Seoul, respectively. To address spatial autocorrelation, a spatial cross-validation approach applying a distance restriction of 100 m between the model training and testing data was employed. The over-optimistic estimates on the predictions were thus prevented, showing model CV-R² of 0.91, 0.67, and 0.78 respectively in Taipei, Osaka, and Seoul. The comparisons between the shortest-distance and lowest-exposure routes showed that the largest percentage of reduced averaged PM₂.₅ exposure could reach 32.1% with the distance increases by 37.8%. Given the findings in this study, routing behavior should be encouraged. With the daily commuting trips expanded, the cumulative effect may become significant on the chronic exposures over time. Therefore, a route planning tool for reducing the exposures shall be developed and promoted to the public.

In China, PM₂.₅ pollution has caused extensive death and economic loss. Thus, an accurate assessment of the spatial distribution of these losses is crucial for delineating priority areas for air pollution control in China. In this study, we assessed the PM₂.₅ exposure-related health effects according to the integrated exposure risk function and non-linear power law (NLP) function in 338 prefecture-level cities in China by utilizing online monitoring data and the PM₂.₅ Hindcast Database (PHD). Our results revealed no significant difference between the monitoring data and PHD (p value = 0.66 > 0.05). The number of deaths caused by PM₂.₅-related Stroke (cerebrovascular disease), ischemic heart disease, chronic obstructive pulmonary disease, and lung cancer at the national level estimated through the NLP function was 0.27 million (95% CI: 0.06–0.50), 0.23 million (95% CI: 0.08–0.38), 0.31 million (95% CI: 0.04–0.57), and 0.31 million (95% CI: 0.16–0.40), respectively. The total economic cost at the national level in 2016 was approximately US$80.25 billion (95% CI: 24.46–132.25). Based on a comparison of Z statistics, we propose that the evaluation results obtained using the NLP function and monitoring data are accurate. Additionally, according to scenario simulations, Beijing, Chongqing, Tianjin, and other cities should be priority areas for PM₂.₅ pollution control to achieve considerable health benefits. Our statistics can help improve the accuracy of PM₂.₅-related health effect assessments in China.

Water pollution is an urgent problem that needs to be controlled via green transformation and the development of the Yangtze River Economic Belt (YREB). Based on the water pollutant discharge and socio-economic database of prefecture-level cities in the YREB from 2011 to 2015, this study explores the spatiotemporal variations in water pollutant discharge in the YREB via two main indicators: chemical oxygen demand (COD) and ammonia nitrogen (NH₃–N). Further, the spatial effects and determinants of water pollutant discharge are quantitatively estimated. The results show that (1) the water pollutant discharge in the YREB has decreased significantly, with the COD and NH₃–N discharge reduced by 10.46% and 10.79%, respectively, and the discharge reduction in the lower reaches was the most prominent; (2) the spatial pattern of water pollutant discharge in the YREB was generally stable and partially improved, and cities with a high rate of water pollutant reduction in the YREB were distributed in the main stream region of the Yangtze River and the intersection of the main stream and tributaries; (3) spatial effects had a significant impact on water pollutant discharge in the YREB, with regional cooperation and economic radiation through environmental management and control initially showing a combined reduction trend in regional water pollutants; and (4) determinants of population size and agricultural economic share declined to varying degrees at the end of the study period, although the urbanization level continued to increase, indicating that urbanization in the YREB occurred too quickly and that water pollutant discharge reduction was limited. However, economic development leading to the deterioration of the water environment was alleviated. In addition, foreign direct investment (FDI) inflows and rapid industrialization processes must be monitored to increase the reduction in characteristic water pollutants.

Studies have reported significant reductions in air pollutant levels due to the COVID-19 outbreak worldwide global lockdowns. Nevertheless, all of the reports are limited compared to data from the same period over the past few years, providing mainly an overview of past events, with no future predictions. Lockdown level can be directly related to the number of new COVID-19 cases, air pollution, and economic restriction. As lockdown status varies considerably across the globe, there is a window for mega-cities to determine the optimum lockdown flexibility. To that end, firstly, we employed four different Artificial Neural Networks (ANN) to examine the compatibility to the original levels of CO, O₃, NO₂, NO, PM₂.₅, and PM₁₀, for São Paulo City, the current Pandemic epicenter in South America. After checking compatibility, we simulated four hypothetical scenarios: 10%, 30%, 70%, and 90% lockdown to predict air pollution levels. To our knowledge, ANN have not been applied to air pollution prediction by lockdown level. Using a limited database, the Multilayer Perceptron neural network has proven to be robust (with Mean Absolute Percentage Error ∼ 30%), with acceptable predictive power to estimate air pollution changes. We illustrate that air pollutant levels can effectively be controlled and predicted when flexible lockdown measures are implemented. The models will be a useful tool for governments to manage the delicate balance among lockdown, number of COVID-19 cases, and air pollution.

On March 12th, 2020, the WHO declared COVID-19 as a pandemic. The collective impact of environmental and ecosystem factors, as well as biodiversity, on the spread of COVID-19 and its mortality evolution remain empirically unknown, particularly in regions with a wide ecosystem range. The aim of our study is to assess how those factors impact on the COVID-19 spread and mortality by country. This study compiled a global database merging WHO daily case reports with other publicly available measures from January 21st to May 18th, 2020. We applied spatio-temporal models to identify the influence of biodiversity, temperature, and precipitation and fitted generalized linear mixed models to identify the effects of environmental variables. Additionally, we used count time series to characterize the association between COVID-19 spread and air quality factors. All analyses were adjusted by social demographic, country-income level, and government policy intervention confounders, among 160 countries, globally. Our results reveal a statistically meaningful association between COVID-19 infection and several factors of interest at country and city levels such as the national biodiversity index, air quality, and pollutants elements (PM₁₀, PM₂.₅, and O₃). Particularly, there is a significant relationship of loss of biodiversity, high level of air pollutants, and diminished air quality with COVID-19 infection spread and mortality. Our findings provide an empirical foundation for future studies on the relationship between air quality variables, a country’s biodiversity, and COVID-19 transmission and mortality. The relationships measured in this study can be valuable when governments plan environmental and health policies, as alternative strategy to respond to new COVID-19 outbreaks and prevent future crises.

Although many researchers have identified the potential psychological benefits offered by greenness, the association between green space structures and mental disorders is not well understood. The purpose of this study was to identify associations between green space structures and the incidence of bipolar disorder. To this end, we investigated 1,907,776 individuals collected from Taiwan’s National Health Insurance Research Database. After a follow-up investigation from 2005 to 2016, among those with no history of bipolar disorder, 20,548 individuals were further found to be diagnosed with bipolar disorder. A geographic information system and landscape index were used to quantify three indices of green space structures: mean patch area (area and edge), mean fractal dimension index (shape), and mean proximity index (proximity). Additionally, greenness indices, the normalized difference vegetation index, and the enhanced vegetation index were used to confirm the association between greenness and incidence of bipolar disorder. These five indices were used to represent the individual’s exposure according to the township of the hospital that they most frequently visited with symptoms of the common cold. Spearman’s correlation analysis was performed to select variables by considering their collinearity. Subsequently, the frailty model for each index was used to examine the specific associations between those respective indices and the incidence of bipolar disorder by adjusting for related risk factors, such as socioeconomic status, metabolic syndrome, and air pollution. A negative association was identified between the mean patch area and the mean proximity index, and the incidence of bipolar disorder. In contrast, a positive association was found between the mean fractal dimension index and the incidence of bipolar disorder. We observed similar results in sensitivity testing and subgroup analysis. Exposure to green spaces with a larger area, greater proximity, lower complexity, and greener area may reduce the risk of bipolar disorder.

Recent epidemiological studies have explored effects of light at night (LAN) exposure on breast cancer, but reported inconsistent findings. We performed a systematic review and meta-analysis of available evidence regarding the association of LAN assessed by satellite data with breast cancer. We conducted a systematic PubMed, Web of Science, and EMBASE database literature search until August 2020. Random-effects meta-analysis was applied to synthesis risk estimates. Heterogeneity was measured using statistics of Cochran’s Q, I², and Tau² (τ²). We assessed publication bias through funnel plot and Egger’s test. Moreover, subgroup analyses according to study design and menopausal status were performed. Risk of bias (RoB) of each included study was assessed using a domain-based RoB assessment tool. The confidence in the body of evidence was appraised using the GRADE approach for level-of-evidence translation. A total of 1157 studies were identified referring to LAN and breast cancer, from which 6 were included for quantitative synthesis. We found a significantly higher odds of breast cancer in the highest versus the lowest category of LAN exposure (OR = 1.11, 95% CI: 1.06, 1.16; I² = 0.0%). In the subgroup analyses stratified by menopausal status and study design, significant association was found in postmenopausal women (OR = 1.07, 95% CI = 1.00, 1.13) and cohort studies (OR = 1.11, 95% CI = 1.05, 1.18), while the summary estimates of premenopausal women and case-control studies showed no significance. The level of evidence for the association of LAN exposure and breast cancer risk was graded as “moderate” with “probably low” RoB according to the NTP/OHAT framework. In conclusion, this study suggests a link of LAN exposure with risk of breast cancer. Further high-quality prospective studies, especially performed in low-to middle-income countries with improvement in the area of LAN exposure assessment are needed to advance this field.

Quantifying methane (CH₄) emissions from paddy fields is essential for evaluating the environmental risks of the paddy rice production system, and improving the accuracy of CH₄ modeling is a key issue that needs to be addressed. Based on a database containing 835 field measurements, both single national and region-specific models were established to estimate CH₄ emissions from paddy fields considering different environmental factors and management patterns using 70% of the measurements. The remaining 30% of the measurements were then used for model evaluation. The performance of the region-specific model was better than that of the single national model. The region-specific model could simulate CH₄ emissions in an unbiased manner with R² values of 0.15–0.70 and efficiency values of 11–60%. The paddy rice type, water regime, organic amendment, latitude, and soil characteristics (pH and bulk density) were identified as the main drivers in the models. By inputting the high-resolution spatial data of these drivers into the established model, the CH₄ emissions from China’s paddy fields were estimated to be 4.75 Tg in 2015, with a 95% confidence interval of 4.19–5.61 Tg. The results indicated that establishing and driving a region-specific model with high-resolution data can improve the estimation accuracy of CH₄ emissions from paddy fields.