Urban Pollution Research Articles

Estimation of land surface temperature and LULC changes impact on groundwater resources in the semi-arid region of Madhya Pradesh, India

The land surface temperature (LST) changes influence on many factors such as land use and land cover (LULC), vegetation and water resources etc. Now a day’s urban population growth, industrial development and human activity are increased due to human migration and urban expansion. The surface temperature is very harmful to vegetation, groundwater level, and ecosystem health. Urban pollution growth and air pollution factor is also playing important role for increase and decrease the LST with climate change impact. Demand of drinking water are increases particular in the urban city due to the population growth and urban expansion, which all of this direct effect on surface and groundwater sources and resources. Hence we have been chosen the latest technologies such as remote sensing (RS), Geographic information System (GIS) and Google Earth Engine (GEE) are very effective for mapping, monitoring and assessment of spatial maps of LST, LULC, and NDVI (Normalised differential vegetation index) estimation. In this study, main focus on the LULC of 2011 and 2021 were classified using random forest (RF) algorithm, ML and remote sensing datasets. LULC were divided into four categories i. e. agricultural land, built up land, waterbody and waste land. For better understanding about the growth of LST, this study examines the association between LST and NDVI change, what vegetation condition impact on the LST. The main and important results have been found the built-up land and LST positive correlation due to increases both the factors in the urban area. LST increases gradually and its bad effect on the green land, water level and expansion of barren land. The range of LST is 43.60 °C to 45.30 °C. Less vegetation cover showing highest LST and vice versa. All of the LST, NDVI, and LULC direct effect on the groundwater availability in the area. This paper important result found the built-up land and LST both are increased in 2021, NDVI and LST having negative correlation. The results of study can very much useful for understanding the LST, NDVI and LULC, these factors what impact on groundwater availability in the study area.

Does exposure timing of macrolide antibiotics affect the development of river periphyton? Insights into the structure and function

Discharged sewage is the dominant source of urban river pollution. Macrolide antibiotics have emerged as prominent contaminants, which are frequently detected in sewage and rivers and pose a threat to aquatic microbial community. As a typical primary producer, periphyton is crucial for maintaining the biodiversity and functions of aquatic ecosystem. However, effects of antibiotic exposure time as well as the recovery process of periphyton remain undetermined. In the present study, five exposure scenarios of two typical macrolides, erythromycin (ERY) and roxithromycin (ROX) were investigated at 50 µg/L, dose to evaluate their potential detrimental effects on the structure and function of periphyton and the subsequent recovery process in 14 days. Results revealed that the composition of periphytic community returned to normal over the recovery period, except for a few sensitive species. The antibiotics-caused significant photodamage to photosystem II, leading to continuous inhibition of the photosynthetic capacity of periphyton. Furthermore, no significant difference in carbon metabolism capacity was observed after direct antibiotic exposure, while the amine carbon utilization capacity of periphyton remarkably increased during the recovery process. These results indicated that periphyton community was capable of coping with the periodic exposure of antibiotic pollutants and recovering on its own. However, the ecological functions of periphyton can be permanently disturbed due to macrolide exposure. Overall, this study sheds light on the influence of macrolide exposure on the development, structure and function of the periphytic microbial community in rivers.

Portable Sensors for Dynamic Exposure Assessments in Urban Environments: State of the Science.

This study presents a fit-for-purpose lab and field evaluation of commercially available portable sensor systems for PM, NO2, and/or BC. The main aim of the study is to identify portable sensor systems that are capable of reliably quantifying dynamic exposure gradients in urban environments. After an initial literature and market study resulting in 39 sensor systems, 10 sensor systems were ultimately purchased and benchmarked under laboratory and real-word conditions. We evaluated the comparability to reference analyzers, sensor precision, and sensitivity towards environmental confounders (temperature, humidity, and O3). Moreover, we evaluated if the sensor accuracy can be improved by applying a lab or field calibration. Because the targeted application of the sensor systems under evaluation is mobile monitoring, we conducted a mobile field test in an urban environment to evaluate the GPS accuracy and potential impacts from vibrations on the resulting sensor signals. Results of the considered sensor systems indicate that out-of-the-box performance is relatively good for PM (R2 = 0.68-0.9, Uexp = 16-66%, BSU = 0.1-0.7 µg/m3) and BC (R2 = 0.82-0.83), but maturity of the tested NO2 sensors is still low (R2 = 0.38-0.55, Uexp = 111-614%) and additional efforts are needed in terms of signal noise and calibration, as proven by the performance after multilinear calibration (R2 = 0.75-0.83, Uexp = 37-44%)). The horizontal accuracy of the built-in GPS was generally good, achieving <10 m accuracy for all sensor systems. More accurate and dynamic exposure assessments in contemporary urban environments are crucial to study real-world exposure of individuals and the resulting impacts on potential health endpoints. A greater availability of mobile monitoring systems capable of quantifying urban pollutant gradients will further boost this line of research.

A Dataset of Distribution and Characterization of Underground Wastewater Treatment Plants in China

Underground wastewater treatment plants (U-WWTPs) have emerged as a novel paradigm for urban wastewater pollutants management, offering benefits such as alleviating the Not-in-my-backyard (NIMBY) effect and utilizing land resources efficiently. China stands at the forefront, witnessing swift advancements in U-WWTP technology and deployment. However, the absence of a thorough understanding of their geographical distribution and operational characteristics could lead to misaligned planning and construction, resulting in inefficient resource allocation and treatment capacities for urban wastewater treatment. This dataset provides an up-to-date overview of the spatial distribution, process selection, and discharge standards for all U-WWTPs in China (with a total number of 201) constructed since 1995. To enhance comparative analysis, the dataset has been supplemented with information on conventional aboveground wastewater treatment plants (A-WWTPs), comprising a total of 2464 records, which enriches a more comprehensive evaluation of different wastewater treatment approaches. Utilizing this dataset can provide essential data support for the strategic management of urban wastewater systems and serve as a valuable reference for the paradigmatic renovation of existing wastewater treatment plants.

Multiscale CFD analysis of urban air pollution dome and ventilation enhancement via an urban chimney

Air pollution episodes are critical environmental challenges in urban areas, primarily linked to the lack of ventilation during calm and stable atmospheric conditions, rather than a significant increase in emissions. The prevailing circulation pattern under these conditions is the Urban Heat Island Circulation (UHIC), which governs the characteristics of the urban pollution dome and the distribution of pollutants. This study aims to assess the UHIC's influence on spatial and temporal variations in pollutant concentrations and to investigate the efficiency of an Urban Chimney (UC) in improving air quality. Due to the interaction of micro- and meso-scale flows, a multi-scale analysis is required. For this purpose, a pressure-based CFD solver is adapted for analyzing airflow and pollutant dispersion in a multi-scale environment. A coordinate transformation method is used to account for meso-scale effects, and the resulting source terms are applied to the solver. The species transport equation is transformed to the new coordinate system, and the effects of species diffusion on enthalpy transport are included in the governing equations. The results demonstrate that UHIC reduces the average pollutant concentration and causes pollution peaks near ground level and at the city center. The interaction of micro-scale flow within the chimney with meso-scale UHIC impacts the flow field and pollutant concentration across the entire domain, decreasing the urban plume's vertical velocity by 40 percent and the mixing height at the city center by 20 percent. It also increases the vertical velocity within the UC by 23 percent. The UC can serve as a controlling tool for urban ventilation, and its capacity to remove pollutants increases sixfold when interacting with UHIC, reducing pollutant concentration citywide.

Runoff Control Performance of Three Typical Low-Impact Development Facilities: A Case Study of a Community in Beijing

The development of sponge cities advocates for sustainable urban rainwater management, effectively alleviating urban flood disasters, reducing non-point-source pollution, and promoting the recycling of rainwater resources. Low-Impact Development (LID) serves as a key strategy in this context, providing essential support for urban rainwater control and pollution reduction. To investigate the runoff control effects of LID measures and to reveal the relationship between facility runoff control performance and installation scale, this study focuses on a sponge community in Beijing. A SWMM model was constructed to analyze the rainwater flood control and pollutant load reduction effects of different LID facilities, including bio-retention cells, green roofs, and permeable pavements. Using evaluation indicators such as surface runoff, node overflow, and pollutant control rates, this study examined how facility performance varies with installation scale under different rainfall conditions. The combination scheme of LID equipment optimal configuration is designed by using multiple criteria decision analysis (MCDA) and cost–benefit theory. The results indicate significant differences in performance among the various LID facilities across different rainfall scenarios. Specifically, the optimal installation proportion for runoff and overflow control of permeable pavements were found to be between 30% and 70%. Green roofs demonstrate superior performance in handling extreme rainfall events, while bio-retention cells exhibit significant effectiveness in controlling Total Suspended Solids (TSSs). Through comprehensive performance evaluation, this study identified the optimal combination scale under a 3-year rainfall recurrence interval as 30% permeable pavements, 20% green roof, and 60% bio-retention cells. This combination effectively leverages the strengths of each facility, ensuring system stability and efficiency while also demonstrating optimal management efficiency in cost–benefit analyses. The findings of this research provide valuable insights for future urban water management and infrastructure development.

Urban air quality index forecasting using multivariate convolutional neural network based customized stacked long short-term memory model

In the context of increasing urban pollution and its adverse health effects, this study focuses on enhancing the air quality prediction model by forecasting future concentrations of various Air Quality Index (AQI) levels to support the development of green smart cities. The primary objective of this study is to develop a robust multivariate time series forecasting model using a combination of Convolutional Neural Networks (CNN) and Customized Stacked-based Long Short-Term Memory (CSLSTM) networks, namely C2SLSTM. The proposed methodology involved preprocessing AQI and meteorological data, scaling the features, and employing a hybrid CNN-LSTM architecture to capture spatial and temporal dependencies in the data. Dynamic AQI pattern changes are computed using online learning. The proposed model was trained on a big dataset containing AQIs such as particulate matter (PM2.5), carbon monoxide (CO), and nitrogen dioxide (NO2), along with meteorological factors like weather count, temperature, wind, humidity, and pressure. Experimental results demonstrate that the model achieved a mean absolute error (MAE) of 0.25, mean square error (MSE) of 0.083, root mean square error (RMSE) of 0.289 and a R2-Score of 0.84 on the test set. Also, compared to Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Stacked Long Short-Term Memory (SLSTM), and Multilayer Perceptron (MLP) models, the C2SLSTM model showcases a performance enhancement of 35 %, 50 %, 30 %, and 32 %. Notably, the CNN component effectively extracted spatial features, while the LSTM layers captured temporal patterns, leading to precise predictions. These results indicate that the proposed approach effectively captures temporal dependencies and improves forecasting performance, with R2-Score values tending towards 1 for 12-hour prediction intervals. This research demonstrates the potential of advanced SLSTM architectures in accurately predicting AQI metrics, which can aid in better environmental monitoring and management. Also, the spatiotemporal correlation analysis is done through the designed ir metric.

Urban mobility and air pollution at the neighbourhood scale in the Megacity of São Paulo, Brazil

Developing countries’ megacities, characterised by dense populations and socioeconomic disparities, often face high levels of pollutants, mainly from vehicle emissions. Poor air quality can lead to a range of public health problems which in turn, need to be addressed by public policies. The main goal of this article is to examine the impact of public policies and the influence of transport modes on air pollution of three districts from the Metropolitan Region of São Paulo, in Brazil: Pinheiros, Parque Dom Pedro II and Taboão da Serra. The method was held through a comparative analysis, which in turn, took into account urban indicators, urban mobility data and pollutants levels (CO and NOx). These data were collected from the 2007 and 2017 São Paulo Metrô Origin and Destination Surveys and the Air Quality Database of the Environmental Company of the State of São Paulo. As key findings, the three study areas’ pollutant concentrations presented a downward trend from 2007 to 2017 as the same time there was an increase in the public transport and non-motorized transport modes. However, it is important to highlight the confluence of the state and federal public policies occuring at the same period such as PRONCOVE, Rodoanel and the Yellow Subway Line. The identified socio-environmental disparities in the urban realm highlight the importance of localised analyses in order to reveal problems and opportunities to get a better response in terms of urban mobility and air quality. Thus, the perpetuation of constant policy’s updates and interdisciplinary collaboration is crucial.

River plasmidome diversity increases with urban associated pollution in the Virilla River in Costa Rica

River plasmidome diversity increases with urban associated pollution in the Virilla River in Costa Rica

Urban greening in Dhaka: Assessing rooftop agriculture suitability using GIS and MCDM techniques

Dhaka ranks among the world's most densely populated cities, with built-up areas expanding to accommodate the demands of a growing population. The rapid urbanization has reduced green space and exacerbated urban heat and pollution in the city. In the quest for a greener and healthier urban environment, rooftop agriculture has emerged as a promising solution, offering opportunities for the restoration of the environment and safe food production. Despite its potential, limited studies have explored the viability of this alternative greening solution for Dhaka. Therefore, this study aims to assess the suitability of rooftops for agricultural activities employing Geographic Information System (GIS) and Multi-Criteria Decision Making (MCDM) techniques. First, seven criteria were selected based on the literature, such as building age, height, rooftop size, building utility, property value, sunlight, and water availability. Second, an expert opinion survey was conducted using the Best Worst Method (BWM) to calculate the criteria's weights. Finally, the suitability map for Dhaka was derived by combining the criteria layers and was subsequently validated. Rooftop area and property value were identified as the most and least important criteria. Approximately 9% (6.27 km2), 68% (46.59 km2), 22% (15.15 km2), and a negligible portion (0.1 km2) of Dhaka city has been classified as highly suitable, suitable, moderately suitable, and not suitable, respectively, for rooftop agriculture. By identifying and promoting the most suitable locations for rooftop agriculture and highlighting existing opportunities, this research will help to initiate and expand sustainable agriculture practices that can contribute to climate change adaptation and urban resilience.

Studying the factors that affect urban road noise-case study: El-Tahrir Street, Al-Dokki Cairo, Egypt.

Expanding road networks to accommodate various activities has significantly increased urban noise pollution, adversely affecting human health and quality of life. Numerous factors influence the noise level in urban areas, including road characteristics, traffic characteristics, surrounding buildings, and weather conditions. While previous studies have considered many of these factors individually, this study aims to integrate all relevant variables to comprehensively monitor and analyze their combined effects on noise levels. The objective is to determine the most influential factors that could be incorporated into effective noise reduction strategies. This research focuses on Cairo, one of the most densely populated cities in the world, where high noise levels are a persistent issue. A detailed case study of Tahrir Street in Dokki, Cairo, provides the basis for this investigation. One of the most crowded areas is El-Tahrir Street in Al-Dokki, which was selected as a case study in this research. This area experiences high traffic volume, with up to 1700 vehicles passing through within a 15-min interval. This significant traffic volume is the primary driver of the elevated noise levels in the area. Traffic and noise level monitoring was conducted using a field survey using the sound level meter.Consequently, a statistical analysis was conducted to investigate the correlation between different factors and the noise level and determine the most influential factors. The study revealed that traffic volume and congestion are the most significant factors influencing noise levels on Tahrir Street, exhibiting strong positive correlations (R = 0.38). Additionally, the study found an inverse relationship between vehicle speed and noise level due to high traffic volumes and identified that building characteristics and wind direction also play roles, albeit to a lesser extent.

A bivariate simultaneous pollutant forecasting approach by Unified Spectro-Spatial Graph Neural Network (USSGNN) and its application in prediction of O3 and NO2 for New Delhi, India

Declining urban air quality affects socioeconomic stability, public health, and ecosystems and is demanding attention of the administration to address environmental sustainability goals. Given the effects of ozone, a greenhouse gas, on local climate and health, this study introduces a Unified Spectro-Spatial Graph Neural Network (USS-GNN) designed for simultaneous 24-hour forecasting of ozone and its precursor, nitrogen-dioxide, while addressing their chemical interactions and spatiotemporal dynamics. This model exploits the graph structure of atmospheric dynamics and mines high-level spatial, spectral, and physical features from atmospheric data through a Dot Product Edge Attention mechanism and a location-aware graph feature rewiring technique. The proposed model is developed for Indian capital city New Delhi, utilizes hourly observations for the years 2021 and 2022 and achieved R2 values of 0.650 and 0.618, RMSE of 13.950 and 16.120 μg/m3, MAE of 10.730 and 12.930 μg/m3 for ozone and nitrogen-dioxide respectively, outperforming state-of-the-art models. The model’s forecast analysis identified error-prone areas, effects of local meteorology, and pollutant interdependencies. An ablation study further detailed the impacts of graph operations on forecasts. Moreover, this study promotes the utility of bivariate modeling frameworks in improving urban pollution monitoring and supporting sustainable city management through data-driven policy implementations.

Sustainable Transportation Solutions for Intelligent Mobility: A Focus on Renewable Energy and Technological Advancements for Electric Vehicles (EVs) and Flying Cars

(1) Introduction: Transportation logistics play a pivotal role in facilitating both individual mobility and supply chain operations on a global scale. However, conventional transportation systems have contributed significantly to urban congestion and environmental degradation. In response to these challenges, there is growing momentum to investigate the potential of renewable energy to power electric vehicles (EVs) and flying automobiles, aiming to reduce fossil fuel dependence and carbon emissions. (2) Objectives: By analyzing key drivers and opportunities for integrating flying vehicles into existing infrastructure through dynamic modeling, this study seeks to accelerate the transition to sustainable transportation. (3) Methodology: A mixed-method approach, combining literature review and causal loop diagram analysis, is employed to understand the factors influencing EV and flying car adoption. (4) Results: Findings highlight the significant potential of renewable energy in reducing emissions and reliance on fossil fuels through widespread EV and flying vehicle adoption. The major drivers and challenges in infrastructure, safety, and airspace management are also identified. In addition, this research emphasizes the importance of sustainable transportation in addressing urban congestion, pollution, and energy security. (5) Conclusions: By leveraging renewable energy and embracing technological advancements, a low-carbon future for transportation can be achieved, benefiting both the environment and society.

Assessment of the urban pollution island intensity in Rome (Italy) from in-situ PM measurements

The Urban Pollution Island (UPI), describing the temporal and spatial distribution of pollutants’ concentration attributed to the presence of urban features and activities, is one of the major problems affecting urban areas and has become more severe with rapid urbanization. To correctly evaluate the UPI Intensity (UPII), i.e., the difference in pollution concentration between the urban agglomeration and its rural surroundings, it is crucial to carefully select rural and, above all, urban reference stations, as local factors such as orography, location of the air quality monitoring stations, and street orientation can significantly impact UPII values. In this work, the UPII in Rome (Italy) is determined using daily-averaged concentrations of PM10 and PM2.5 collected by in-situ stations over the period 2018–2023. Three different methods for the assessment of UPII are tested and compared by varying the sub-set of selected urban stations, according to their environmental classification. The approach proposed will have significant implications on the management of urban environment and on the tailored design of urban air quality improvement strategies.Results show slight differences in the monthly-averaged concentrations of both PM10 and PM2.5 between the “urban traffic” and “urban background” stations, suggesting that the proximity of the emission sources to the monitoring stations moderately influences the concentrations, potentially due to limited ventilation within street canyons, which can inhibit mixing processes. The annual variation of UPII reveals that PM10 is more sensitive to the selection of the stations particularly during winter, when the differences between the three assessment methods reach 100%. Our findings also indicate that, in the case of Rome, using the largest number of stations available in the urban area could enhance the UPII evaluation, taking into consideration the urban structure and the specific characteristics of local emission sources. The results presented here, although related to a single city, demonstrate that the selection of urban stations for the evaluation of UPII is not straightforward and requires further investigation.

Resilience quantification and recovery strategy simulation for urban underground logistics systems under node and link attacks: A case study of Nanjing city

Urban Underground Logistics Systems (UULS) have become an emerging solution to mitigate urban surface traffic congestion, environmental pollution, and surface transport safety risks. However, during the operation of UULS, the use of advanced technologies such as the Internet of Things (IoT) introduces cybersecurity risks to the system. Moreover, severe natural disasters can also cause damage to underground transportation network links. Existing research and planning primarily concentrate on the system design and benefits of UULS, neglecting the system's service level under attack scenarios. This study outlines three representative UULS network prototypes and proposes a resilience quantification method centered on logistics efficiency. It also focuses on comparing the effectiveness of three recovery strategies. These strategies give priority to maximum flow, betweenness centrality, and regional importance, as well as the priority of node and link repairs. The resilience quantification method and recovery strategies are applied in a case study set in Nanjing City. The case study results reveal that the Two-echelon network shows exceptional resilience. Regarding the effectiveness of recovery strategies, the strategy based on maximum flow proves to be the most effective, and focusing on node repair can lead to higher system resilience. Based on these findings, this study offers recommendations to transportation and logistics management decision-makers, focusing on UULS resilience and recovery strategy selection. These recommendations are intended to provide valuable guidance for the planning and design of future UULS, ensuring their resilience and reliability.

Neonicotinoids as emerging contaminants in China's environment: a review of current data.

Neonicotinoids (NEOs), the most widely used class of insecticides, are pervasive in the environment, eliciting concerns due to their hydrophilicity, persistence, and potential ecological risks. As the leading pesticide consumer, China shows significant regional disparities in NEO contamination. This review explores NEO distribution, sources, and toxic risks across China. The primary NEO pollutants identified in environmental samples include imidacloprid, thiamethoxam, and acetamiprid. In the north, corn cultivation represents the principal source of NEOs during wet seasons, while rice dominates in the south year-round. The high concentration levels of NEOs have been detected in the aquatic environment in the southern regions (130.25ng/L), the urban river Sects.(157.66ng/L), and the downstream sections of the Yangtze River (58.9ng/L), indicating that climate conditions and urban pollution emissions are important drivers of water pollution. Neonicotinoids were detected at higher levels in agricultural soils compared to other soil types, with southern agricultural areas showing higher concentrations (average 27.21ng/g) than northern regions (average 12.77ng/g). Atmospheric NEO levels were lower, with the highest concentration at 1560pg/m3. The levels of total neonicotinoid pesticides in aquatic environments across China predominantly exceed the chronic toxicity ecological threshold of 35ng/L, particularly in the regions of Beijing and the Qilu Lake Basin, where they likely exceed the acute toxicity ecological threshold of 200ng/L. In the future, efforts should focus on neonicotinoid distribution in agriculturally developed regions of Southwest China, while also emphasizing their usage in urban greening and household settings.

Nonlinear effect of urban noise pollution on depression of the elderly in China based on the Bayesian machine learning method

With the intensification of urbanisation and population ageing globally, it is crucial to understand the effect of urban noise pollution on the mental health of the ageing population. This research employed the Bayesian machine learning method to investigate the nonlinear effect of urban noise pollution on depression scores in the elderly (DSE) population in China. Based on individual survey data, the China Health and Retirement Longitudinal Study (CHARLS) data from 2011 to 2020, and corresponding temporally monitored annual urban equivalent sound pressure level (ESPL) data in China, propensity score matching (PSM) was used to identify the causal effect of urban ESPL on DSE. The quantile regression model (QRM), the Bayesian additive regression tree model (BARTM), and the Bayesian causal forest method (BCFM) were used to detect the nonlinear influencing mechanism of urban ESPL on DSE. The PSM analysis which can balance confounding influence revealed a significant disparity in depression scores between ageing individuals exposed to higher ESPL and those residing in more tranquil settings, suggesting a definitive causal relationship. Furthermore, the QRM results highlight a significantly increasing effect of ESPL on DSE at higher quantiles (0.4528 and 0.5709 at the 75th and 85th percentiles, respectively). The BARTM identifies a ‘U-shaped’ dose–response curve between ESPL and DSE, with low ESPL as beneficial, but exposure above 53.67 dB(A) representing noise pollution, especially past 55.85 dB(A), significantly increasing DSE. Specifically, when ESPL ranges from 53.67 to 55.68 dB(A) with other variables held constant, DSE increases by an average of 0.28 for every 1 dB(A) rise. Beyond 55.68 dB(A), the average DSE increase is 0.85 for each additional 1 dB(A) in ESPL. A notable spatial heterogeneity was observed in the local effects of annual urban ESPL on DSE. The BCFM results indicate that socio-economic exposure level can modulate the effect of ESPL on DSE.

Identification of critical socioeconomic factors influencing the characteristics of urban nonpoint source pollution during a period of industrial transformation

As an effective measure to promote socio-economic development, industrial transformation indirectly changes the characteristics of urban underlying surfaces, leading to the complexity of controlling nonpoint source (NPS) pollution in cities. However, the relationship between industrial transformation and urban NPS pollution, as well as the critical socioeconomic factors influencing the characteristics of urban NPS pollution during the period of industrial transformation, is still unclear, seriously affecting urban water environment management. In this research, indicator systems for the estimation of industrial transformation and urban NPS pollution were first established. Then, the characteristics of industrial transformation and urban NPS pollution were analyzed through an entropy weight method and a volume–concentration model. In addition, critical socioeconomic factors affecting urban NPS pollution during periods of industrial transformation were identified using Pearson correlation analysis and a grey correlation model. Using Dongguan City in South China as the study area, the results showed that the level of industrial transformation in this city shows an upward trend, and the towns with relatively high level of industrial transformation gradually gathered around Songshan Lake. The four urban NPS pollution index (i.e., chemical oxygen demand, suspended solids, total nitrogen, and total phosphorus) significantly increased during the slow transformation period (2000–2010), then slightly decreased during the rapid transformation period (2010–2020). The pollution loads exhibited significant spatiotemporal heterogeneity. A significantly negative correlation (p < 0.05) was observed between industrial transformation and urban NPS pollution levels during the transformation period. It was found that fertilizer, electricity, and water consumptions, as well as permanent population were the critical socioeconomic factors affecting urban NPS pollution. Control of the application amounts of fertilizers and improvement in the efficiencies of urban water and electricity use are the effective methods for preventing and controlling urban NPS pollution during the period of industrial transformation. In addition, industrial enterprises should increase their investment in research and development activities to accelerate industrial transformation and reduce the urban NPS pollution load. This research contributes to urban NPS pollution control in the area undergoing industrial transformation.

Female leaders as environmental saviors: Tackling urban pollution in Chinese cities

In the Chinese political context, local government officials have substantial influence over the implementation of policies. The approach to urban environmental management varies significantly between female and male officials, which in turn affects governance outcomes differently. Consequently, the gender of local officials plays a crucial role in shaping urban environmental conditions. This study investigates the impact of female officials on urban environmental pollution in China over the period from 2003 to 2018. Utilizing data from the China Urban Statistical Yearbook combined with individual profiles of officials at the prefecture level, the analysis reveals a notable decrease in urban environmental pollution associated with female leadership. Notably, the effect is more pronounced among younger female officials compared to their older counterparts, and those with higher education levels are particularly effective in curbing pollution emissions. The pollution abatement effect of female officials is found to be stronger in the central regions compared to the eastern and western regions. The robustness of these findings is confirmed through checks against alternative explanatory variables, permutation tests, and treatment effect analyses. Overall, this research contributes new perspectives on management strategies that can sisgnificantly improve urban environmental quality in China.

Examining temporal trends in heavy metal levels to analyze sediment pollution dynamics in the Saida urban watershed (N-W Algeria).

The study focuses on current pollution in the Saïda basin, a semi-arid region in north-western Algeria. By analyzing sediments, the study provides interesting results on urban pollution and its environmental impact. The research consists of two main phases, each addressing different aspects of pollution. In the first phase, different pollution indicators are used to analyze heavy metals and organic pollutants in urban drainage sediments. The results are compared with sediment quality guidelines, regulatory thresholds, and local and international references. Most of the metallic contaminants exceed the toxicity levels established by the continental crust and sediment quality guidelines, suggesting an anthropogenic origin. In addition, contamination indices show significant accumulation. In this context, the results highlight the importance of accumulation and transport processes in urban sediments. Hydrological parameters significantly influence heavy metal distribution mechanisms. Remarkable variations between copper (Cu) and lead (Pb) suggest a combined or singular source during transport. Conversely, chromium (Cr), nickel (Ni), and iron (Fe) are mainly derived from natural lithological sources. Cadmium (Cd) is associated with anthropogenic sources related to the agricultural use of phosphate fertilizers, whereas zinc (Zn) is mainly derived from physical corrosion processes. In the second phase, a combined descriptive and multivariate statistical analysis examines the mobility and distribution of heavy metals and their relationships with organic matter (OM) over time. Pronounced temporal variations in Cd, Zn, and Cu concentrations are attributed to human activities. Strong correlations exist between OM and cobalt (Co), Cu and Pb, confirming the ability of OM to adsorb these metals under specific geochemical conditions associated with waste disposal. Conversely, Zn, Cd, Cr, and Ni show weak or negative correlations with OM, suggesting diverse sources, including potential agricultural, industrial, and natural origins. The dendrogram confirms the existence of previously identified contaminant groups, suggesting common sources and potential co-occurrence patterns. This analysis highlights the role of the drainage network as a physico-chemical reactor in the mobilization of contaminants. It underlines the importance of sediment interactions in urban pollution processes. Finally, recommendations are proposed to ensure effective pollution control and remediation. PRACTITIONER POINTS: Useful information on pollution and its environmental impact is provided by the analysis of sediments in the urban basin of Saida (NW-Algeria). The results of this study indicate high levels of heavy metals in the sediments, in excess of toxicity limits, and evidence of anthropogenic sources. Temporal variations in metal concentrations indicate the influence of human activities. The study has made it possible to identify the sources, to understand the mobility and distribution, and to control the contamination by heavy metals in the urban sediments. Drainage system serves as a pathway for dispersing contaminants.