Bin Zou

Mobile monitoring of air pollution has been extensively employed in environmental and epidemiological studies. However, previous monitoring campaigns, which typically involved only one or two vehicles operating during temporally incomplete and imbalanced daytime, were limited in spatiotemporal coverage, resolution, and representativeness. This leaves important gaps in understanding how air pollution varies spatiotemporally across urban on-road microenvironments, as well as in assessing the impact of fleet size and temporal sampling incompleteness (daytime/weekday/weekend-skewed vs 24 h continuous and full week) on monitoring coverage and PM_2.5 estimates, respectively. Here, we fill these gaps by employing a citywide taxi-based mobile monitoring fleet of over 200 taxis operating 24 h a day over a two-week period and equipped with fine particulate matter (PM_2.5) sensors to characterize PM_2.5 spatiotemporal variability and quantify traffic-related increment contributions across diverse urban on-road microenvironments. Leveraging this large-scale, spatiotemporally dense, and representative data set, we further investigated how fleet size influences monitoring coverage and repeated visits and how temporal sampling incompleteness and imbalance affect monitoring outcomes and bias. Our results show spatial and temporal heterogeneity in on-road PM_2.5 levels and traffic-related increment contributions across microenvironments. Increasing fleet size improves spatial coverage but exhibits diminishing returns, and temporally incomplete and imbalanced sampling can introduce biased outcomes, particularly at finer temporal resolutions. These findings have important implications for refined urban air quality management and epidemiological research, thereby offering guidance and insights for future studies.

Global warming and rapid urbanization are intensifying the threat of mosquito-borne diseases, prompting many cities to default to large-scale chemical spraying as an emergency response. However, this quick-fix approach may conceal profound ecological collateral damage that has long been overlooked. Here, we systematically map the impacts of mass larviciding and adulticiding on urban ecosystems and present a novel ecological risk indicator framework that tracks acute nontarget effects, the environmental fate and transport of active ingredients, impairment of ecosystem functions, and human health exposure. Building on this diagnostic toolkit, we propose a tiered, climate-smart, and city-tailored governance model that integrates environmental management, biological regulation, intelligent sensing, and civic engagement. By shifting mosquito control from reactive chemical dependence to an ecologically informed paradigm, this approach offers both a conceptual architecture and a practical roadmap for ensuring public health safety without compromising urban ecological sustainability.

The application of remotely sensed PM_2.5 concentration datasets has become increasingly widespread, but the spatial precision verification at local scales is lacking. This study aims to investigate the consistency of PM_2.5 concentration between remotely sensed data and ground-based data and optimize the accuracy of remotely sensed PM_2.5 concentration data at the urban scale. Specifically, taking Changsha city as a case, four evaluation indices—R², RMSE, uncertainty, and high deviation rate (HDR)—were employed to evaluate the credibility of remotely sensed data at national and dense ground-based stations, then analyze spatial variations of credibility and develop a Recursive Feature Elimination–Cross-Validation Random Forest (RFECV-RF) model to improve local fitting accuracy. Results show that remotely sensed data exhibit high credibility at national stations, while credibility at dense stations varies spatially and tends to decline with increasing distance from national stations. After optimizing by the RFECV-RF model, the credibility of remotely sensed data can be significantly improved, with R² increasing from 0.87 to 0.98, RMSE decreasing from 8.59 µg/m³ to 3.08 µg/m³, HDR reducing from 2.01% to 0.04%, and uncertainty declining from 18.93% to 8.27%. Nevertheless, certain regions still require additional monitoring to further expand the credible spatial extent. These findings provide valuable insights for improving PM_2.5 concentration remote sensing monitoring methods and designing the integrated “air–space–ground” observational network scheme.

Air pollution of PM_2.5 and O₃ is a global health concern. Traditional approaches for identifying air pollution control areas mainly relied on pollutant concentrations, neglecting population distribution and exposure. This study proposes a method to divide these areas from a health risk perspective, comparing their objectivity and rationality with the government-defined key regions. The results show that for PM_2.5, the health risk population and average risk rates in the prevention and control areas were 0.993 million (0.1286%), 1.030 million (0.1283%), and 1.023 million (0.1202%) in 2010, 2015, and 2020, significantly higher than in the key areas: 0.778 million (0.1252%), 0.834 million (0.1278%), and 0.825 million (0.1212%). Similarly, for O₃, the figures in the prevention and control areas were 0.096 million (0.01228%), 0.095 million (0.01243%), and 0.110 million (0.01316%), also higher than in the key areas: 0.0757 million (0.01218%), 0.078 million (0.01189%), and 0.090 million (0.01315%). Additionally, the Gini coefficients for PM_2.5, O₃, and overall health risks in the prevention and control areas were lower (0.182, 0.203, 0.284) compared to those in the key areas (0.207, 0.216, 0.292). This study provides a method for defining air pollution control regions based on health risks, offering significant insights for pollution zoning and prevention strategies

Satellite retrievals can capture the spatiotemporal variation of O₃ over a large area near the surface. However, due to the unstable functional relationships between variables across spatiotemporal scales, the outlier predictions will reduce the accuracy of the prediction model. Therefore, a validated residual constrained random forest model (RF-RVC) is proposed to estimate the monthly and annual O₃ concentration datasets of 0.1° in China from 2005 to 2020 using O₃ precursor remote-sensing data and other auxiliary data. The temporal and spatial variations of O₃ concentrations in China and the four urban agglomerations (Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD) and Sichuan–Chongqing (SC)) were calculated. The results show that the annual R² and RMSE of the RF-RVC model are 0.72~0.89 and 8.4~13.06 μg/m³. Among them, the RF-RVC model with the temporal residuals constraint has the greatest performance improvement, with the annual R² increasing from 0.59 to 0.8, and the RMSE decreasing from 17.24 μg/m³ to 10.74 μg/m³, which is significantly better than that of the RF model. The North China Plain is the focus of ozone pollution. Summer is the season of a high incidence of ozone pollution in China, YRD, PYD, and SC, while pollution in the PRD is delayed to October due to the monsoon. In addition, the trend of the O₃ and its excess proportion in China and the four urban agglomerations is not satisfactory; targeted measures should be taken to reduce the risk of environmental ozone. The research findings confirm the effectiveness of the residual constraint approach in long-term time-series modeling. In the future, it can be further extended to the modeling of other pollutants, providing more accurate data support for health risk assessments.

Heavy metal pollution of farmland is worsened by the excessive introduction of heavy metal elements into soil systems, posing a substantial threat for global food security and human health. The traditional laboratory-based methods for monitoring soil heavy metals are limited for large-scale applications, while hyperspectral imagery data-based methods still face accuracy challenges. Therefore, a fusion XGBoost model based on the superposition of ensemble learning and packaging methods is proposed for large-scale monitoring with high accuracy of soil heavy metal using hyperspectral imagery. We took Xiong’an New Area, Hebei Province, as the study area, and acquired heavy metal content using chemical analysis. The XGB-Boruta-PCC algorithm was used for precise feature selection to obtain the final modeled spectral response features. On this basis, the performance indicators of the Optuna-optimized XGBoost model were compared with traditional linear and nonlinear models. The optimal model was extended to the entire region for drawing the spatial distribution map of soil heavy metal content. The results suggested that the XGB-Boruta-PCC method effectively achieved double dimensionality reduction of high-dimensional hyperspectral data, extracting spectral response features with a high contribution, which, combined with the XGBoost model, exhibited greater general estimation accuracies for heavy metal (Pb) in farmland (i.e., Pb: R² = 0.82, RMSE = 11.58, MAE = 9.89). The results of the mapping indicated that there were exceedances for the southwest and parts of the west over the research region. Factories and human activities were the potential causes of heavy metal Pb contamination in farmland. In conclusion, this innovative method can quickly and accurately achieve monitoring large-scale soil heavy metal contamination in farmland, with ZY-1-02E spaceborne hyperspectral imagery proving to be a reliable tool for mapping soil heavy metal in farmland.