Source classification of municipal solid waste (MSW) is widely implemented as an upstream strategy; however, its environmental and health implications across the waste management system remain insufficiently quantified. This study develops an integrated system-level framework linking waste flow redistribution to multi-media emissions, probabilistic health risks assessment, and monetized health impacts. Shenzhen, a large Chinese megacity, is examined as a case study under three scenarios: complete sanitary landfilling (S0), preliminary source classification with landfill dominance (S1), and enhanced classification with incineration dominance (S2). From S0 to S2, landfill dependence declines from 100% to 8.9%, while resource recovery rises to 38.9%. Compared with S0, S2 reduces conventional pollutant emission factors by over 90%, reflecting a shift to centralized and controlled treatment. Children's non-carcinogenic risk decreases from 1.22 × 10−1 to 1.31 × 10−3, and the 31.1% probability of exceeding the 10−6 carcinogenic risk benchmark is eliminated. Annual carcinogenic-related hospitalization costs decline by more than 99%, and the VSL-based economic health benefits avoided mortality reach USD $8.08 × 105. Overall, integrating source classification with optimized downstream treatment substantially reduces system-level environmental emission and delivers measurable public health gains.

Abstract. Fast and timely estimation of changing air pollutant emissions is critical for understanding the complex sources of air pollution and supporting air quality improvement, while current regional emission inventory was commonly reported with time lag or coarse temporal resolution. Here we developed a near-real-time approach that calculates the daily emissions of anthropogenic air pollutants, and applied this approach for Jiangsu province, a typical developed region in eastern China. We estimated that the annual total anthropogenic emissions of SO2, NOX, primary fine particles (PM2.5), non-methane volatile organic compounds (NMVOCs), and NH3 were 246, 727, 298, 1186, and 377 Gg, respectively, for Jiangsu in 2022. Compared to the national emission inventory, application of the provincial-level daily emission estimates provided better model performance of PM2.5 and ozone (O3) simulation for all the involved months. The NOX, SO2, PM2.5, and NMVOCs emissions in Jiangsu during April–May 2022 (the period of COVID-19 lockdown in Shanghai) were respectively 8 %, 6 %, 6 %, and 10 % smaller than those in the same period of 2023. Transportation and Industry respectively contributed 89 % of NOX emission reduction and 93 % NMVOCs reduction. Combining with machine learning algorithms, moreover, we revealed that the changing agricultural NH3 emissions dominated the variability of daily PM2.5 concentration, and that off-road transportation contributed substantially to variabilities of both PM2.5 and O3 levels. The study proved advantages of incorporation of near-real-time data and machine learning techniques on tracking the fast-changing emissions and detecting the sources of varying air quality.

As the world’s largest tobacco producer, China faces potential environmental and health challenges from tobacco (Nicotiana tabacum L.) curing, an intensive yet poorly quantified and weakly regulated emission source. Based on continuous emission monitoring system (CEMS) measurements and field tests, this study develops a 2010–2022 prefecture-level emission inventory for carbon dioxide (CO2), sulfur dioxide (SO2), nitrogen oxides (NOX), carbon monoxide (CO), particulate matter (PM), and volatile organic compounds (VOCs) from the tobacco curing industry, and projects mitigation pathways to 2060. Emissions peaked in 2013, and by 2022 reached 5.1 Mt CO2, 19.6 kt SO2, 10.4 kt NOX, 81.2 kt CO, 3.3 kt PM, and 0.2 kt VOCs. These emissions are comparable to or exceeding those from energy-intensive industries like coking during tobacco curing season. Spatial clustering in Southwest China close to rural residential settlements implies non-negligible air and health impacts. These exposure concerns underscore the need for well-designed mitigation policies grounded in a clear understanding of emission trajectories and their driving factors. Our scenario analysis demonstrates that an integrated-emission-mitigation (IEM) strategy could achieve near carbon neutrality by 2060, alongside reducing air pollutants by over 60%, primarily through fuel substitution. These findings underscore production-side measures, particularly fuel substitution, as essential for synergistic carbon and air pollution mitigation in this fugitive source, while also alleviating environmental health burden in nearby communities.

Severe ozone (O3) pollution is a major challenge for air quality improvement in China, primarily due to its spatiotemporal heterogeneity and nonlinear formation regimes. Here, we developed a novel framework to illustrate the spatiotemporal evolution of pollution episodes and to identify their recurrent driver modes and formation sensitivities in Jiangsu Province during the warm season from 2020 to 2023. The framework integrated joint analysis of the synoptic weather type (WT) and the regional spatiotemporal pattern (SP) of O3 episodes, explainable machine learning, and geostationary satellite observation of O3 precursors. We showed that O3 pollution episodes occurred preferentially with a limited number of WT × SP combinations, particularly those involving subtropical high-pressure systems coincident with pollutant accumulation patterns in southern Jiangsu. Three key driver modes (thermal-driven, radiation-moisture synergy, and transport-accumulation) were identified, with significant associations with specific WT × SP combinations. The transport-accumulation mode commonly corresponded to the VOC-limited regime, while thermal-driven and radiation-moisture synergy modes shifted O3 formation toward the NOx-limited regime more frequently. This work demonstrated an effective, efficient, and adaptable tool of detecting the source and evolution of multiple O3 episodes and could support flexible and timely design of the pollution control strategy, with an improved understanding of heterogeneous and changing causes of O3 episodes.

The rapid proliferation of incineration plants has rendered their carbon emissions a substantial contributor to urban carbon emissions. Municipal solid waste classification presents waste-to-energy plants with challenges and opportunities. Monitoring nine Shenzhen incineration plants (2018-2022), we employed Monte Carlo simulations for sensitivity analysis and constructed response surface models to derive quantitative emission reduction pathways. Results indicate the plastic incineration proportion rose to 27.6%, increasing direct emissions and carbon substitution credits from electricity. Reducing plastic by 1% cuts emissions by approximately 17.38 kg CO2-eq/t. Plastic should maintain below 34% considering synergistic effects among dominant emission determinants: plastic composition, grid carbon intensity, and electricity output. Singular interventions targeting plastic reduction or energy efficiency improvements have limited mitigation potential. A comprehensive mitigation scenario combining efficiency improvement, combined heat and power, plastic waste reduction, enhanced recycling, and Bioplastic substitution enables net negative emissions under future low-carbon grids. This study highlights that, systemic transformation from carbon source to carbon sink necessitates coordinated actions across multistakeholder.

Under the combined influences of urbanization, policy adjustments, and ecological restructuring, China’s rural landscapes are undergoing rapid transformation. Taking Anlong Village in Pidu District, Chengdu, as a case study, this paper analyzes the spatiotemporal evolution of a typical agroforestry settlement landscape—characterized by traditional “linpan” (forest-enclosed) villages—from 1995 to 2025, using remote sensing data, landscape pattern indices, and actor-network theory (ANT). The results indicate that land-use transformation has undergone three phases: traditional agriculture, specialized horticultural operations, and multifunctional landscape integration. Cultivated land area has continued to decrease, while construction land has expanded steadily, and forest land has exhibited fluctuating changes. ANT analysis reveals that through key events such as property rights reform and land transfer, a core network system comprising the government, enterprises, and cooperatives has been formed, incorporating the participation of multiple stakeholders. Smooth power flows, effective interest negotiation, and mechanisms for resolving conflicts have driven the landscape toward an orderly restructuring characterized by ecological centralization and industrial decentralization. Essentially, this landscape pattern directly reflects the spatial manifestation of the governance efficacy of actor networks. This study constructs a dynamic, coordinated socio-spatial model that deepens our understanding of the dynamic coupling between policy, spatial patterns, and ecological value and provides a decision-making basis for balancing environmental sustainability and rural development within the framework of the rural revitalization strategy.

The emissions of non-methane volatile organic compounds (NMVOCs) worsen air quality and pose potential health and environmental risks. The steel industry is an important NMVOCs source category, yet studies on NMVOCs emissions in steel industry remains limited, particularly for stainless steel manufacturing. This study presents a field investigation of NMVOCs emissions for different processes in both stainless steel and carbon steel plants, revealing clear differences in emission characteristics between them. The results show that the NMVOCs emissions were higher in stainless steel production, with notable different species profiles compared to carbon steel plant. The emission of carbon disulfide during the sintering process in stainless steel manufacturing was lower compared to carbon steel production. These discrepancies were attributed to the different raw materials used for production of the two types of steel, and higher fuel consumption and lower combustion efficiency for stainless steel production. The NMVOCs emission factor for stainless steel sintering was 40 times higher than that of carbon steel sintering, thus the NMVOCs emissions from steel production could be underestimated without consideration of this high emission factor. In addition, this study examined the impact of air pollution control devices (APCDs) on NMVOCs emissions for steel production. Selective catalytic reduction (SCR) and flue gas desulfurization (FGD) technologies demonstrated great NMVOCs removal efficiency, while fabric filter (FF) might elevate NMVOCs emissions. The diverse effects resulted primarily from the removal mechanisms of these APCDs and the physicochemical properties of NMVOCs. Through field measurements, this study improves the understanding of NMVOCs emission characteristics in the steel manufacturing industry and provides valuable insights for development of NMVOCs emission control strategies.

Recycling waste milk cartons is being tested in several cities in China, but its environmental and economic effects have yet to be investigated systematically. This study examined a practical system of complete waste milk carton recycling and assessed the system considering life cycle environmental impacts, particularly carbon emissions and cost-benefits. The results show that electricity consumption contributes 42 % of the environmental burden, and water consumption contributes 22 %. Still, paper, plastic, and aluminum recovery counteract the negative impact, making the normalized life cycle impact reach -5.15E-15 based on the environmental footprint (EF) method. Similarly, the substitution effects of recycled materials can counteract the total carbon emissions, and the net emissions are -0.011 kg CO2 per unit carton corresponding to 1 L milk, implying an excellent carbon trade opportunity when taking incineration or landfill as the baseline. Saving electricity and water and using clean power can significantly improve the system's environmental benefits. However, due to the small scale of this practical project, the system cannot generate net profit, and labor costs and land rent are the majority. Carbon trade, government subsidies, and extended producer responsibility are the recommended management strategies for improving waste milk carton recycling.

Anthropogenic nitrogen (N) fertilization is projected to increase N export through the land-to-ocean aquatic continuum (LOAC), threatening aquatic and coastal ecosystems. Counterintuitively, long-term monitoring revealed declining or stable dissolved inorganic nitrogen (DIN) concentrations in many regions over the past three decades, despite a ~30% surge in global N fertilizer use. Here, we resolve this paradox by integrating ~86,400 observational records with machine learning and atmospheric deposition modeling. We demonstrate that atmospheric oxidized N (OXN) deposition, rather than fertilizer use, is the dominant driver of LOAC N dynamics. Stringent air quality policies that reduced OXN deposition by 54–56% in the United States and Europe have cut down the trajectory of LOAC N pollution. In China, an initial rise in LOAC N concurrent with increasing OXN deposition was reversed following clean air actions. Conversely, India's unchecked OXN drive continued N deterioration. Projections to 2060 confirm that OXN deposition control under a sustainable scenario will reduce LOAC N fluxes, even as fertilizer inputs rise. Our findings establish targeted OXN emission abatement as a pivotal and efficient strategy for mitigating inland and coastal eutrophication, thereby reconciling agricultural productivity with aquatic ecosystem health.

Reactive mercury (RM) has a short atmospheric residence time of hours to weeks, leading to primarily local deposition. Gaseous elemental mercury (GEM) has a residence time of 0.3-1 year, resulting in long-range transport. The Reactive Mercury Active System (RMAS) developed by the University of Nevada, Reno (UNR) is a membrane-based Hg-monitoring system. This study presents the first independent evaluation of RMAS outside the UNR group, focusing on its performance in polluted environments. The impacts of key factors on the accuracy of RMAS and the deconvolution method for thermal desorption profiles were evaluated. Nylon membranes exhibit variable capture efficiencies (12-81%) of RM across compounds. Consequently, corrections based on multiple linear regression were utilized to enhance RM compound identification. In polluted environments with high fine particulate matter (PM2.5) concentrations (annual average concentration >10 μg m-3), the sampling flow rate did not affect the RM concentration but could alter its chemistry via ion exchange. Uncertainties were estimated to be ±19% for the overall concentration and ±6-47% for dominant RM forms. This work showed that RMAS is a promising tool for speciated RM monitoring, supporting the effectiveness evaluation of the Minamata Convention.

Data-driven identification of multiscale governing equations remains challenging because coefficient magnitudes depend on candidate-term scaling, even though their contributions to the equation balance remain unchanged. We develop Balance-Guided Sparse Regression (BG-SR), a weak-form framework that ranks candidate terms by rescaling-invariant contributions. Informative integration windows are selected using QR-factorization-based discrete empirical interpolation, over which weak-form integrals of the time-derivative and candidate terms are evaluated to construct the regression system, while support cardinality is represented through a term-wise \(\ell_{2,0}\) measure. Progressive backward elimination then generates a nested model path ordered by contributions to the equation balance, and the model size is selected \textit{a posteriori} from residual jumps. Numerical experiments on the Burgers, Korteweg--de Vries, modified Kuramoto--Sivashinsky, and two-dimensional reaction--diffusion equations show that BG-SR accurately retains dynamically significant terms with small coefficients. For a two-dimensional unsteady laminar boundary-layer flow at \(\mathrm{Re}=10^{6}\), BG-SR recovers the local near-wall streamwise momentum balance while retaining the wall-normal viscous term with a coefficient of order \(10^{-6}\). Comparisons with representative sparse-identification methods and noise tests further demonstrate robust recovery of both global governing equations and spatially localized dominant balances.

A new type of natural gas reservoir—bauxite reservoir was firstly discovered in the Longdong area in the southwestern Ordos Basin, central China. However, the sedimentary environments of these karst bauxites in the area remain unclear, constraining the research on the formation mechanism and distribution prediction of the bauxite reservoirs. In this study, the sedimentary environments of the bauxite series from five exploration wells were investigated by an integrated analysis including biomarkers, sedimentogenic Sr/Ba ratio and pre-Carboniferous paleogeomorphology. The Pr/Ph ratios of the bauxitic claystones are all less than 1, indicating a reducing sedimentary environment. The bauxitic claystones from CT3 well are characterized by C20 < C21 > C23TT pattern and C27S/C29S < 1.0, whereas those from other sampling wells are characterized by C20 < C21 < C23TT pattern and C27S/C29S > 1.0. The sedimentogenic Sr/Ba ratios of the bauxite series in CT3 well range from 0.13 to 0.45, markedly lower than those in HT7 and L58 wells which vary from 1.75 to 28.47. The pre-Carboniferous paleogeomorphology shows that CT3 well is located in the karst plateau, whereas the other sampling wells are located in the karst depressions.The bauxite series in the karst plateau were deposited in continental facies, such as intermountain freshwater lakes or pools. In contrast, those in the karst depressions were deposited in the marine-continental transitional facies (including open sea, bays and coastal wetlands), in which the bauxite was deposited in seawater or saltwater during the regressive phase, while the bauxitic claystones were deposited in brackish, saline, and marine environments. These indicate that the bauxite in the area maybe secondary bauxite via allochthonous deposition of bauxite fragments, which can be attributed to the prolonged exposure history for about 140 Ma.

Fast and timely estimation of air pollutant emissions is critical for understanding the complex sources of air pollution and supporting air quality improvement, while current emission inventory was commonly reported with time lag or coarse temporal resolution. Here we developed a near-real-time approach that calculates the daily emissions of anthropogenic air pollutants, and applied this approach for Jiangsu province, a typical developed region in eastern China. We estimated that the annual total anthropogenic emissions of SO2, NOx, primary fine particles (PM2.5), non-methane volatile organic compounds (NMVOCs), and NH3 were 246, 727, 298, 1186, and 377 Gg, respectively, for Jiangsu in 2022. Compared to available national emission inventory (MEIC), application of the provincial-level daily emission estimates provided better model performance of PM2.5 and ozone (O-3) simulation for all seasons (represented by January, April, July and October). The NOx, SO2, PM2.5, and NMVOCs emissions in Jiangsu during April-May 2022 (the period of COVID-19 lockdown in Shanghai) were respectively 8 %, 6 %, 6 %, and 10 % smaller than those in the same period of 2023. Transportation and Industry respectively contributed 89 % of NO(x )emission reduction and 93 % of NMVOCs reduction. Combining with machine learning, moreover, we revealed that the changing agricultural NH3 emissions dominated the variability of daily PM2.5 concentration, and that off-road transportation contributed substantially to variabilities of both PM2.5 and O-3 levels. The study proved advantages of incorporation of near-real-time data and machine learning techniques on tracking the fast-changing emissions and detecting the sources of varying air quality.

Severe ozone (O3) pollution is a major challenge for air quality improvement in China, primarily due to its spatiotemporal heterogeneity and nonlinear formation regimes. Here, we developed a novel framework to illustrate the spatiotemporal evolution of pollution episodes and to identify their recurrent driver modes and formation sensitivities in Jiangsu Province during the warm season from 2020 to 2023. The framework integrated joint analysis of the synoptic weather type (WT) and the regional spatiotemporal pattern (SP) of O3 episodes, explainable machine learning, and geostationary satellite observation of O3 precursors. We showed that O3 pollution episodes occurred preferentially with a limited number of WT & times; SP combinations, particularly those involving subtropical high-pressure systems coincident with pollutant accumulation patterns in southern Jiangsu. Three key driver modes (thermal-driven, radiation-moisture synergy, and transport-accumulation) were identified, with significant associations with specific WT & times; SP combinations. The transport-accumulation mode commonly corresponded to the VOC-limited regime, while thermal-driven and radiation-moisture synergy modes shifted O3 formation toward the NOx-limited regime more frequently. This work demonstrated an effective, efficient, and adaptable tool of detecting the source and evolution of multiple O3 episodes and could support flexible and timely design of the pollution control strategy, with an improved understanding of heterogeneous and changing causes of O3 episodes.

Climate change and associated human response are supposed to greatly alter surface ozone (O3), an air pollutant generated through photochemical reactions involving both anthropogenic and biogenic precursors. However, a comprehensive evaluation of China's O3 response to these multiple changes has been lacking. We present a modeling framework under Shared Socioeconomic Pathways (SSP2-4.5), incorporating future changes in local and foreign anthropogenic emissions, meteorological conditions, and biogenic volatile organic compound (BVOC) emissions. From the 2020s to 2060s, daily maximum 8 h average (MDA8) O3 concentration is simulated to decline by 7.7 ppb in the warm season (April–September) and 1.1 ppb in the non-warm season (October–March) over the country, with a substantial reduction in exceedances of national O3 standards. Notably, O3 decreases are more pronounced in developed regions such as Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) during the warm season, with reductions of 9.7, 14.8, and 12.5 ppb, respectively. Conversely, in the non-warm season, the MDA8 O3 in BTH and YRD will increase by 5.5 and 3.3 ppb, partly attributed to reduced NOx emissions and thereby a weakened titration effect. O3 pollution will thus expand into the non-warm season in the future. Sensitivity analyses reveal that local emission change will predominantly influence future O3 distribution and magnitude, with contributions from other factors within ±25 %. Furthermore, the joint impact of multiple factors on O3 reduction will be larger than the sum of individual factors, due to changes in the O3 formation regime. This study highlights the necessity of region-specific emission control strategies to mitigate potential O3 increases during the non-warm season and under the climate penalty.

Source apportionment of volatile organic compounds (VOCs) provides scientific basis for the prevention of ozone (O3) and particulate matter pollution, while bias exists in the receptor model based on the measured concentration. We conducted online measurement of hourly ambient concentrations for multiple VOCs species in January and July 2023 in Lianyungang, a city with fast growing industry in east China, and five major sources of VOCs were identified with an improved application of the positive matrix factorization (PMF) model by considering photochemical losses and formation potential of secondary aerosols (SOAP) and O3 (OFP). For January and July, the total VOCs (TVOC) concentrations were measured at 23.7 +/- 10.8 and 12.0 +/- 7.5 ppbv, and the averaged consumed VOCs due to photochemical losses in daytime reached 3.12 and 3.08 ppb, respectively. The concentrations were generally lower than those in Chinese mega cities. As the important emission from industrial sources, Oxygenated VOCs (OVOCs) were found to occupy a significant position in the concentration structure of TVOCs and photochemical losses in Lianyungang. Inclusion of photochemical losses avoided underestimation of contribution from solvent usage and overestimation of vehicle emissions in summer. There was a significant difference between source contributions based on concentrations and environmental impacts (SOAP in winter and OFP in summer). For the latter, coal burning was identified as the most important source in January, and industrial emissions and solvent usage in July. Indicated by the backward trajectories, local emissions were the dominant contributor to the VOCs pollution, mainly from the large ports and industrial parks, while industrial emissions from nearby provinces (Shandong and Anhui) were also important. The analyses provide scientific evidences for determining the priorities of VOCs emission controls for cities with fast industrialization.