This study takes Guilin, a typical tourist city, to explore its carbon emission characteristics. It integrates multi-source remote sensing data (satellite CO2 column concentration, NO2, nightlight data, etc.), social statistics, geographic information of the road network, and constructs a 10-meter high-resolution spatial allocation product of CO2 emissions for Guilin (2019-2024). The Logarithmic Mean Divisia Index (LMDI) is then adopted to quantify tourism-related driving factors.Under the most stringent buffered block cross-validation, the CatBoost model achieved an RMSE of 0.0010 kg CO2 m-2 s-1, an MAE of 0.0006 kg CO2 m-2 s-1, and an R2 of 0.8225, while showing comparatively stable error performance across the spatially explicit validation schemes. Spatially, elevated predicted CO2 emission flux density was concentrated around major tourism and transportation activity areas, including Liangjiang International Airport and the Lijiang River tourism corridor. Relative to ODIAC, the 10 m spatial allocation relocated the principal airport-related hotspot toward the actual airport area.COVID-19 mobility restrictions coincided with a decrease in mean CO2 emission flux density of 2.31 × 10-5 kg CO2 m-2 s-1 in 2019–2020. The LMDI decomposition indicates that contraction of the tourism-scale effect was the dominant emission-reducing contribution during this interval. A modest decrease in mean CO2 emission flux density was observed after 2023; however, the limited time series does not support attribution of this change to any specific policy intervention.The LMDI decomposition shows that industrial structure upgrading (cumulative contribution: −1.98 × 10-4 kg CO2 m-2 s-1) was the largest emission-reducing effect, whereas tourism-scale expansion (+8.60 × 10-5 kg CO2 m-2 s-1) partially offset this reduction. These contributions represent changes in the mean CO2 emission flux density over the study area. The results highlight a “scale–structure” trade-off in tourism-related carbon emissions.This spatial allocation product can identify dispersed emission hotspots in tourism cities and provide methodological support for fine-scale spatial analysis. The results provide evidence for considering zone-specific emission management and differentiated low-carbon transition strategies in tourism-oriented cities, while the specific policy instruments require further feasibility assessment.
This article presents the results of long-term air quality measurements (2015–2024) from the Helsinki Traffic Supersite, including gaseous compounds (NO, NO2, CO, CO2, O3), particulate matter (PM2.5, PM10, particle number (PN) and size distribution), chemical composition of PM (black carbon (BC), organic aerosol (OA), inorganic species), lung-deposited surface area (LDSA), polycyclic aromatic hydrocarbons, volatile organic compounds (VOC), as well as road surface conditions and meteorology. In addition to the long-term observations, large number of targeted short-term measurement campaigns were conducted to investigate emerging phenomena, to further develop supersite measurements and gain new information about sources impacting air quality.The observed air quality improvements at the Traffic Supersite were driven by declining traffic exhaust concentrations (NOx (-8.0%/yr), BC (-7.1%/yr), PN (-3.7%/yr), and anthropogenic VOCs (-3.1 to -8.9%/yr)). The observed emission factors (g/kgfuel) also decreased for NOx (-7.6%/yr), BC (-7.5%/yr), and PN (-4.1%/yr), reflecting fleet renewal. The observed rate of decrease in PN concentration was lower than that of other parameters, particularly in the smallest size classes (< 30 nm). Organic aerosol analysis showed that traffic hydrocarbon tracers (m/z 57) declined faster (-7.9%/yr) than total OA and oxidized OA (m/z 44), which are linked to secondary formation and long-range transport. Long-term results indicate that compliance with the upcoming EU Air Quality Directive limit values for 2030 is already largely achievable, with the most challenging aspect being the exceedances of the PM10 daily limit due to road dust events. However, achieving the EU Zero Pollution target for 2050 remains challenging, and the WHO guideline values for PM10, PM2.5 and NO2 continue to be exceeded. These unique findings highlight the importance of comprehensive, long-term supersite measurements for understanding pollutant sources and trends, and for supporting urban planning and policy development.
Maritime transport is a major component of global trade and an important source of atmospheric pollutants and greenhouse gases. In the decarbonization era, ship emission research is moving beyond conventional emission estimation toward integrated assessment frameworks that connect high-resolution inventories, atmospheric transformation, health and ecosystem impacts, climate forcing, and mitigation pathways. This review focuses primarily on characterizing and quantifying shipping-related primary emissions and emission inventories, which constitute the source term for atmospheric impact assessment. Ambient concentrations and environmental impacts are further governed by plume dispersion, transport, chemical transformation, and secondary pollutant formation. Using a PRISMA-style screening process, this review synthesizes recent advances in ship emission sources, inventory methodologies, spatiotemporal patterns, atmospheric impacts, and air pollution–climate co-mitigation strategies. The evolution of ship emission inventories is first reviewed, from fuel-based top-down approaches to activity-based bottom-up methods and AIS-driven high-resolution models, with emphasis on emission factors, load factors, operating-mode identification, auxiliary-engine and boiler emissions, uncertainty quantification, and multi-source validation. The review then summarizes the spatial and temporal characteristics of ship emissions, highlighting global shipping lanes, regional chokepoints, port-city interfaces, coastal corridors, straits, and island regions. Atmospheric processes and environmental impacts are further examined, including primary pollutants, secondary PM2.5 formation, nonlinear O3 chemistry, population exposure, health risks, atmospheric deposition, and climate forcing. The evidence indicates that ship-related impacts are shaped not only by emission magnitude, but also by chemical regimes, meteorology, receptor proximity, fuel quality, and policy context. Mitigation policies and energy-transition pathways are critically assessed, including international regulations, emission control areas, shore power, vessel speed reduction, alternative marine fuels, market-based measures, and Well-to-Wake life-cycle assessment. Overall, ship emission control is shifting from single-pollutant regulation toward integrated air pollution–climate co-mitigation. Future research should prioritize uncertainty-aware high-resolution inventories, observation-constrained emission verification, coupled air-quality–health–climate–economic assessment, AI-enhanced emission modeling, and region-specific strategies for coastal, port, strait, and island environments. An integrated data-to-impact-to-policy framework is essential for supporting cleaner, healthier, and lower-carbon maritime transport.
Black carbon (BC) is a major light-absorbing component of atmospheric particulate matter with well-documented effects on climate and human health, yet it remains poorly characterised across south-eastern Europe. This study presents the first systematic multi-station analysis of equivalent BC (eBC) source apportionment in Croatia, based on four years (2022–2025) of measurements from a national network of eight AE33 seven-wavelength aethalometers at five urban-background and three rural-background stations spanning continental, sub-Mediterranean, and Mediterranean environments. Fossil-fuel (eBC_ff) and biomass-burning (eBC_bb) fractions were resolved using the two-component aethalometer model. Annual mean eBC ranged from 0.28 μg m−3 at the cleanest rural station (Polača) to 1.92 μg m−3 at the most polluted urban station (Slavonski Brod), where winter biomass-burning fractions reached 73% and concentrations approached those of larger Balkan agglomerations such as Bucharest. A pronounced coastal–continental contrast was observed: continental urban stations (Slavonski Brod, Osijek, Zagreb) showed strong winter maxima dominated by residential combustion (winter eBC_bb of 62–73%), whereas coastal Mediterranean stations showed weak seasonality (eBC_bb below 34% year-round) and persistent traffic dominance. The eBC_ff component was stable and season-independent across all urban stations, identifying road traffic as a persistent, non-seasonal source, while diurnal profiles resolved traffic (twin rush-hour peaks) and evening residential-heating fingerprints. These findings establish an empirical baseline for BC in a data-sparse region of Europe and indicate that effective air quality management requires geographically differentiated strategies: prioritizing the residential energy transition in continental inland cities, where solid-fuel heating dominates the winter burden, while addressing traffic emissions in coastal urban centers.
Escalating air pollution and the limitations of conventional forecasting approaches necessitate advanced models for accurate Air Quality Index (AQI) prediction. This study proposes four hybrid deep learning architectures—Model-1: Hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM), Model-2: Hybrid LSTM– Deep Neural Network (DNN), Model-3: Hybrid GRU–CNN, and Model-4: Hybrid Conv1D–DNN, that integrate convolutional feature extraction, recurrent temporal modeling, dense representation learning, and attention mechanisms to capture complex temporal dependencies and nonlinear interactions among multiple pollutant and meteorological variables. The four-year daily data from Delhi, consisting of major pollutants such as PM2.5, PM10, NO2, NOx, CO, benzene, and meteorological variables like temperature, relative humidity, and atmospheric pressure, is employed. Optimized models have been developed by considering different loss functions and learning rates. Low prediction error (RMSE ≈ 36.29) and high correlation (R ≈ 0.9) have been achieved. Model-1 has shown superior performance compared to other models. Significant reduction in error has been observed. The performance of the model has been tested for different seasons, showing robustness in high variability during winter. The performance of the model has also been tested on different city data, such as Ghaziabad and Faridabad. Although the accuracy of the model can be increased by considering more complex models, the results have shown a good trade-off in accuracy and complexity, thus establishing the robustness of the framework. Moreover, accurate AQI forecasting can support public health by enabling timely air quality warnings and informed environmental decision-making.
Global climate change and air pollution are closely linked environmental challenges. As the most abundant greenhouse gas, CO2 requires further investigation in relation to co-occurring air pollutants. Based on hourly observations from 16 monitoring-site groups across Europe, the United States, China, and Canada, this study examines the temporal and seasonal variability of CO2 and six air pollutants, including CO, NO2, SO2, PM2.5, PM10, and O3. The study periods differ among site groups and are defined by the common valid observation periods of CO2 and the six pollutants, with an overall temporal coverage from 2014 to 2025. Pearson correlation analysis quantified the relationships between CO2 and air pollutants, hierarchical clustering classified monitoring sites according to correlation patterns, and stepwise multiple regression assessed the effects of time scale and pollutant concentrations on CO2 variability. Results show that CO2 exhibits a clear seasonal cycle, with higher concentrations in winter and lower in summer. CO and NO2 show seasonal variations consistent with CO2, whereas O3 displays an opposite pattern (r: -0.83∼-0.09). The coupling between particulate matter (PM) and CO2 strengthens in winter but weakens in summer. CO2 is positively correlated with most pollutants except O3 in winter, while correlations decline substantially in summer. Cluster analysis separates the sites into anthropogenic emission-dominated and natural source-regulated groups. Stepwise regression (the maximum R2 is 0.92) indicates positive within-period linear tendencies and seasonal variability in CO2 at most sites, although model performance is limited where geographical and vegetation-related factors are excluded.
The Taklimakan Desert, the world’s second-largest shifting-sand desert, is a major dust source for northwestern China and East Asia, and its emissions contribute substantially to the global dust cycle. Therefore, improving dust forecasts over this region is essential. To address limited applicability of the wind-profile functions in the Revised MM5 surface-layer scheme over the Taklimakan Desert, we recalibrated the scheme’s stability parameters using turbulence and gradient measurements from the Tazhong station, and evaluated the impacts through sensitivity experiments. The estimated von Kármán constant (k) averages 0.41, close to the canonical value (0.40). The recalibrated stability parameters (γm = 19.0 for unstable conditions; βm = 5.8 for stable conditions) fall within expected theoretical ranges. Using the revised parameters improves the correlation coefficient(R) of 10-m wind speed at 62% of stations, while reducing the mean absolute error (MAE) and root mean square error (RMSE) at 76% and 71% of stations, respectively, with particularly notable improvements during the morning and afternoon transition periods during March–May 2020. For dust concentrations, PM10 MAE and RMSE decrease by 1–6.5% across all stations. For PM2.5, the R increases by 1–4%, and MAE and RMSE decrease by 1–6.8%. Aerosol optical depth (AOD) decreases by 0.1–0.2 over the northwestern and eastern parts of the desert, effectively reducing the model’s systematic high bias in dust simulation.
Low-molecular-weight nitrogen-containing organic compounds (LMW NOCs) from biomass burning (BB) significantly influence aerosol chemistry, yet their fuel-dependent molecular signatures and formation pathways remain poorly understood. Here, we combined molecular characterization with compound-specific nitrogen isotope analysis of amino acids and LMW NOCs in raw biofuels and combustion-derived aerosols from herbaceous, softwood, and hardwood fuels burned in a traditional household stove. Herbaceous-burning aerosols showed higher semi-quantitative relative abundances of six-membered N-heterocycles, especially 3,6-dimethyl-2,5-hydroxypyrazine, whereas wood-burning aerosols were characterized by higher relative abundances of aminophenols, particularly 2-aminophenol.Strong correlations between amino-acid pools and LMW NOCs, together with Rayleigh-type δ15N enrichment of free Gly (y = 3.0 – 8.6×ln(1-f), r2 = 0.41, p < 0.05) indicate a stronger linkage between proteinaceous-material thermal transformation and LMW NOC formation during herbaceous combustion, whereas wood smoke likely involves additional or alternative pathways. Furthermore, similar δ15N values of adenine in raw fuels and aerosols are consistent with partial direct transfer or survival during combustion. These findings reveal distinct fuel-dependent LMW NOC signatures and provide molecular and isotopic constraints on nitrogen transformation in fresh BB aerosols.
Portable wind tunnels (PWTs) offer advantages over commonly used devices for measuring odorous gas emissions from passive liquid surfaces, such as wastewater stabilization ponds and settling basins, as they enable controlled flow conditions and more representative estimates of emission fluxes. A fundamental and largely unexplored challenge remains: how to prescribe and reproduce field-representative shear conditions within confined portable systems. This is particularly relevant because friction velocity () plays a dominant role in controlling emission rates, especially for compounds with high gas-phase mass-transfer resistance. PWTs partially enclose the air–liquid interface to quantify emission fluxes while attempting to mimic atmospheric flow conditions. In this study, the internal flow within a PWT is experimentally characterized and modelled to evaluate boundary-layer development and friction velocity. High-resolution Particle Image Velocimetry measurements were conducted under a range of operating conditions. Results show that friction velocity can be predicted using boundary-layer theory. At low flow rates (mean velocities 0.12–0.61 m s-1), consistent with previous studies, the flow remains laminar and is well described by a modified Blasius solution. At moderate flow rates (1.92–2.27 m s-1), the flow is turbulent, with values comparable to field conditions, and the boundary layer is represented by an adjusted turbulent power-law profile. A key outcome is the development of predictive relationships enabling direct prescription of friction velocity through operating flow rate. This work bridges atmospheric boundary-layer flows and PWT conditions, supporting ongoing discussions on the standardization of PWTs for field odour emission measurements and providing a framework for physically based, reproducible emission studies.
Fixed-site regulatory monitoring and mobile monitoring approaches are each effective in capturing temporal and spatial variations in air pollution, respectively, but limited in addressing both dimensions simultaneously. This study proposed a novel approach using a rotating-site trailer to capture both dimensions for source and health risk apportionment in the Puget Sound region from 2024 to 2025. Multiple air pollutants, including PM2.5, BC, NO2, CO2, and size-resolved particle number concentrations (PNC) were sequentially measured across four overburdened communities identified based on ambient air pollution concentrations, health impacts, and demographics. Positive matrix factorization (PMF) was used to characterize the underlying sources and corresponding health risks for each community separately. A total of five common factors were derived, which were interpreted as fresh transportation emissions (gasoline and aircraft), diesel exhaust, transportation-related urban background, heavy fuels, and aged background mix. This study reveals that transportation-related sources contributed 32-43% of PM2.5 exposure and corresponding risks and accounted for most of the CO2 and NO2 concentrations. In contrast, heavy fuels and aged background mix dominated the PNC of ultrafine particles larger than 42 nm. The findings can lay a foundation for estimating the health benefits of various source control strategies for these overburdened communities in the future.
Estimates of exposure inequality depend both on where air pollution is measured and on how inequality is summarised from those measurements. Environmental-justice surveillance typically compresses a pollutant’s socioeconomic gradient into one regression slope, the slope index of inequality (SII), defined as the population-weighted slope of group-mean concentration on income rank. A single slope assumes that the concentration–rank profile is a straight line and that exposure and income move together within every income group. The SII still returns a number when either assumption fails, but it no longer flags the misfit. We characterise county-level exposure inequality across the contiguous United States for four criteria pollutants (PM2.5, NO2, O3, SO2) over 2000–2023 (up to 44 states). We add two dimensionless shape diagnostics to the standard slope. The Middle-Group Residual (MGR) is the signed departure of the middle-income stratum from the SII line, and the Within-Stratum Slope Heterogeneity (WSSH) is the spread of within-stratum exposure–income correlations. PM2.5 exposure uses a population-weighted hybrid of regulatory monitors and satellite-derived concentrations; the other pollutants use the monitor network. The mean income-stratified relative index of inequality (RII) is −0.048 (PM2.5), +0.278 (NO2), −0.018 (O3), and +0.147 (SO2). The PM2.5 gradient reverses sign between the monitor-equipped county subset (RII +0.074, lower-income more exposed) and the population-representative full-county sample (RII −0.048). This reversal directly reflects the siting of the monitoring network. Non-linear profiles dominate; |MGR| exceeds 0.02 in 50%–92% of state-years and rises monotonically at finer partitions. The SO2 gradient reversed direction over the period (HAC p=0.003). Its remaining burden along the racial-composition axis falls on lower-income but predominantly White rural counties, so the income and racial axes diverge. A single slope cannot reveal this divergence. Across these four pollutants, the slope alone is therefore an incomplete summary. Its sign can hinge on which counties host monitors, and its straight-line form fails in most state-years. The MGR and WSSH recover the missing shape and coverage information from the same county data the SII already uses.
Ambient air pollution remains a leading environmental risk factor globally. Over the past decade, China has achieved marked reductions in particulate matter (PM) following the 2013 Air Pollution Prevention and Control Action Plan; however, surface ozone (O3) has increased in multiple urban agglomerations, signalling a transition from single-pollutant to complex multi-pollutant regimes. At the western terminus of the Fenwei Plain—a national key region designated in 2018—systematic, county-level assessments of long-term air-quality evolution are lacking for Baoji City. This study presents a nine-year (2017–2025) analysis of six criteria pollutants (SO2, NO2, CO, O3, PM2.5, and PM10) across all 12 administrative divisions (four districts and eight counties) of Baoji, using continuous monthly data from 16 national and provincial monitoring stations (n = 108 months). Spearman rank correlation, Mann–Kendall trend tests, and Sen’s slope estimators were applied to (i) quantify the significance of inter-annual concentration trends, (ii) characterise the spatial decoupling between particulate matter and ozone at the county level, and (iii) document the timing and magnitude of the regime shift in dominant pollution types. CO, NO2, PM10, and PM2.5 exhibited statistically significant downward trends at >90 % of monitoring sites (p < 0.05), with cumulative reduction rates exceeding 45 %, 46 %, 51 %, and 53 %, respectively. Annual mean SO2, NO2, and CO continuously met the National Grade-II Standard from 2020 onward, while PM2.5 achieved stable compliance from 2023 to 2025. The 90th percentile of daily maximum 8-hour O3 (O3-8h-90th) remained below the Grade-II limit of 160 μg/m3 throughout the study period, despite a phased rebound during 2022–2024. Spatially, PM2.5 and PM10 followed a west-low / centre-east-high distribution with a continuously shrinking regional gradient, whereas O3 maintained a stable east-high / south-low pattern, reflecting persistent photochemical pressure in the eastern plain counties. The proportion of O3 as the primary pollutant rose from 15 % in 2017 to 28 % in 2025, marking a regime shift from PM-dominated to O3–PM2.5 co-dominated air pollution. These findings provide empirical evidence for synergistic pollution–carbon governance and inform seasonally differentiated, location-specific control strategies in Baoji and analogous industrial basin cities.
Regulated emissions in a type approval test (TAT) and a tailpipe solid particle number (SPN) concentration measurement, which has been introduced in the periodic technical inspection (PN-PTI) in some European countries, were measured for a diesel passenger car equipped with a diesel particulate filter (DPF). The correlations between SPN in TAT and the PN-PTI were good, and the European recommended limit for PN-PTI was almost equal to the regulatory limit of SPN in the TAT, suggesting the validity of evaluating exhaust performance by PN-PTI. In addition, in a real-world evaluation campaign conducted at several PTI stations in Japan, exhaust emissions from light-duty and heavy-duty vehicles under inspection were monitored by PN-PTI devices. A total of 582 DPF diesel vehicles were checked, and 18.2 % of them were found to have problems with their DPFs.
We report on an experimental study regarding the performance of state-of-the-art ventilation systems with different inlet positions with respect to the aerosol spread. For that, the spatial distribution of aerosol exposure was determined for six different source locations in a generic passenger compartment. This allowed us to evaluate the different concepts in terms of parameters such as contaminant removal efficiency, the number of seats above a certain threshold or the mean particle concentration in the breathing zone of the passengers. The results revealed strong differences in the local particle concentrations depending on the source position. Further, it was found that two ceiling-based concepts, microjet ceiling and ceiling mounted slot diffusers aimed at the passengers, have significant advantages over the other concepts, especially when it comes to the number of seats with aerosol exposures above certain thresholds. Yet, which concept is optimal still depends on the chosen threshold. The contaminant removal efficiency fluctuates only weakly around 0.5 for the different concepts, revealing mixing-ventilation principles for all concepts.
Background Climate change is increasingly recognized as a significant driver of vector-borne diseases, with urban areas being particularly vulnerable to the dual pressures of rapid urbanization and environmental shifts. Methods A comprehensive literature review and data analysis were conducted to assess the impact of climate variables, such as temperature and precipitation, on the dynamics of vector populations in urban environments. Epidemiological data from multiple global cities were analyzed, alongside climate model projections, to forecast future trends in vector-borne disease transmission under various climate scenarios. Results Our findings demonstrate a clear correlation between climate change and the expansion of vector populations in urban areas, with temperature increases and altered precipitation patterns driving the proliferation of mosquitoes and other vectors. Urban centers in tropical and subtropical regions, such as Southeast Asia and Sub-Saharan Africa, are predicted to experience a higher incidence of diseases like malaria, dengue, and chikungunya. Novelty and Contribution This study is among the first integrative reviews to focus specifically on urban ecosystems, combining epidemiological data, vector ecology, and climate projections (RCP4.5 and RCP8.5) with policy case studies. By explicitly linking climate scenarios with disease outcomes in urban contexts, our work provides new insights into underexplored vulnerabilities, particularly in informal settlements, and highlights actionable strategies for strengthening urban health resilience. Discussion The study emphasizes the need for integrated urban health strategies that combine climate adaptation, enhanced vector control, and public health infrastructure. Policymakers must prioritize vulnerable populations in informal settlements and invest in climate-resilient urban planning to mitigate disease risks.
Rapid urban growth in Africa is worsening air pollution in many cities, yet limited air-quality monitoring infrastructure hinders accurate assessment of residents’ exposure. In this study, we applied a land-use regression (LUR) model to estimate annual ambient fine particulate matter (PM2.5) concentrations and assess population exposure in Addis Ababa, Ethiopia. Estimated annual PM2.5 concentrations showed substantial spatial variability, ranging from 5.9 to 47.9 μg/m3, with a citywide average of 17.3 μg/m3. No part of the city met the World Health Organization (WHO) annual air quality guideline of 5 μg/m3, and 77% of the area exceeded Interim Target 3 (IT-3; 15 μg/m3). The highest concentrations, exceeding the lowest threshold (IT-1, 35 μg/m3), were observed in the sub-cities of Arada, Addis Ketema, Lideta, and Kirkos, where dense urban development and major transportation corridors are concentrated. The LUR model demonstrated strong performance, with an adjusted R2 of 0.74 and a root mean square error (RMSE) of 1.85 μg/m3. Key predictors included proximity to waste dump sites, green space coverage, and the lengths of primary and secondary roads, reflecting major pollution sources and land-use patterns associated with reduced emissions or improved pollutant dispersion. Model diagnostics showed good internal validity, with low multicollinearity among predictors and no evidence of spatial autocorrelation in the residuals. We further quantified population exposure to PM2.5 by calculating population-weighted concentrations at both city and kebele (the lowest administrative unit) levels. The population-weighted citywide mean concentration was 21.3 μg/m3, while kebele-level exposures ranged from 13 to 30 μg/m3, highlighting significant disparities in pollution burden across the city. Overall, ambient PM2.5 concentrations substantially exceeded WHO guideline levels, underscoring the urgent need for stronger air quality policies, targeted emission reductions, and urban planning strategies to promote healthier, more equitable living environments.
Large amounts of aerosols containing harmful components are released into the atmosphere during the burning of household waste. This activity greatly impairs air quality on a local scale, sometimes making it unbearable near the sources, which often leading to public complaints. The available methods to detect waste burning are based on the analysis of the metal content in the remaining ash sometimes well after the activity in question. In this study, we present the development and the principles of a method for real-time detection of waste burning from the flue gas at the sources, intended as a proof of concept. In the laboratory experiments, different types of waste were co-combusted with firewood in a stove, with waste doses added at different stages of the firewood burning process to simulate the variable combustion conditions present under real-world burning scenarios. Numerous flue gas parameters were measured during the experiments from which the most representative ones were selected to enable the identification of waste burning on site. In the 3D spaces defined by different selected parameters, we identified the region that is characteristic of waste burning. Random forest models using nine parameters were also optimized, trained and tested to identify the burning of different fuel types. The co-burning of plastic and plastic containing wastes (PET, PE, PUR, PP, OILYRAG, PVC and PS), their mixtures, and shoes with firewood was identified as waste burning with high accuracy (83–99%). The flaming combustion phase of composite wood panels (furniture panels and oriented strand boards) and the flaming phase of painted wood burning were also correctly recognised as waste-related combustion within this accuracy range (97-98%). The burning of wastes containing natural materials was identified with accuracies of 75% for rags and 57% for Tetra Pak. Firewood combustion was reliably recognised, with an accuracy of 83-84%. Overall, the results demonstrate that the model can reliably distinguish between authorized and waste-containing fuels based on flue gas parameters, under the investigated combustion conditions. The developed method provides a basis for the future development of a cost-effective real-time detection approach for identifying domestic waste burning, especially where conventional tracers cannot be applied and rapid determination is required.
Fine particulate matter (PM2.5) pollution remains a persistent air quality problem in northern Thailand, where seasonal biomass burning and regional meteorological conditions frequently lead to elevated concentrations. This study quantified exposure to PM2.5 and associated health risks in the upper northern region of Thailand and evaluated potential health benefits under different PM2.5 reduction scenarios. Daily PM2.5 concentrations from 14 air quality monitoring stations during 2020–2024 were analyzed to derive annual mean exposure levels. Health impacts among adults aged ≥30 years were estimated using established concentration–response relationships to calculate relative risk (RR) and attributable fraction (AF) for cardiopulmonary and lung cancer mortality. The regional annual mean PM2.5 concentration was 40.42 μg m-3, substantially exceeding international air quality guideline levels. Under baseline exposure conditions, estimated RR values were 1.44 (95% CI: 1.14 - 1.81) for cardiopulmonary mortality and 1.72 (95% CI: 1.22 - 2.42) for lung cancer mortality. Corresponding AF estimates indicated that 30.56% (95% CI: 12.28 - 44.75) and 41.86% (95% CI: 18.03 - 58.68) of cardiopulmonary and lung cancer mortality risk, respectively, were attributable to PM2.5 exposure. Scenario analyses showed progressive reductions in RR and AF with decreasing PM2.5 concentrations, with the most stringent reduction scenario yielding mean AF reductions exceeding 20% for both health outcomes. These results indicate a consistent pattern between PM2.5 concentration scenarios and mortality outcomes and suggest potential reductions in estimated health risks associated with lower PM2.5 concentrations.
East Asia critically relies on precipitation for freshwater, yet faces severe anthropogenic aerosol pollution. Understanding whether and how aerosols regulate regional rainfall is therefore essential. Using decade-long coupled chemistry–meteorology simulations, we quantify the impacts of total anthropogenic aerosols and their major components on precipitation and disentangle the underlying mechanisms. Aerosols suppress rainfall through two coupled pathways. First, reduced surface solar radiation cools the lower troposphere and stabilizes the boundary layer, weakening convective available potential energy. This triggers anomalous mid-tropospheric subsidence and a weakened low-level monsoon circulation, effectively throttling moisture supply to East Asia. Second, aerosol–cloud interactions enhance low-level clouds and suppress high-level clouds in the subtropics. Quantitatively, indirect cloud-mediated effects dominate, producing an ∼34% precipitation reduction relative to the clean-air baseline, while direct radiative effects contribute a ∼30% decrease. The precipitation response is highly sensitive to aerosol composition: sulfate and organic aerosols drive most rainfall suppression through cloud redistribution, whereas black carbon partly offsets this effect via atmospheric heating and enhanced convection. These findings highlight the critical role of aerosol composition in regulating precipitation and freshwater availability across East Asia.
