Search Results (1,097)

Recent studies have shown that common household activities produce characteristic patterns in indoor air pollutants, enabling activity inference using environmental measurements alone. However, pollutant-based approaches are usually formulated as flat multi-class classification problems, even though indoor environments are dominated by long baseline periods with no emission-generating activity, leading to false alarms and unstable predictions. This work proposes a gated hierarchical inference framework for recognizing activities from indoor air quality data. A first-stage gate detects whether a time window contains activity-induced pollutant dynamics, while a second-stage classifier conditionally identifies the specific activity only when activity relevance is detected. Multivariate time-series measurements of particulate matter, volatile organic compounds, nitrogen oxides, carbon dioxide, temperature and relative humidity were collected using a portable monitoring system during controlled household cooking and cleaning experiments. Temporal windows were processed using recurrent neural network models in both stages. By separating activity detection from activity identification, the proposed method aligns inference with the physical generation of indoor pollutant signals and improves robustness in baseline-dominated monitoring scenarios while maintaining reliable discrimination among activities. The framework supports unobtrusive activity recognition and enables applications in exposure-aware monitoring and intelligent indoor environmental management. Full article

The black carbon (BC) emission resulting from human activity comes mainly from fossil fuels and solid biomass burning, as well as transport fuels due to incomplete combustion. The biggest sources of BC pollution are currently diesel transport and domestic heating appliances burning solid fossil fuels or biomass. Firewood and pellet fuels were used for this BC research. The study used four domestic heating appliances using wood and agricultural waste pellets, as well as several types of firewood. The tests used a gravimetric particulate analysis method to determine the total amount of particulate matter. In further physical and chemical analyses, the emissions are broken down into components, i.e., substances of known composition that can be separated from the sample and weighed. In our study, the BC emissions varied from 0 to 120 mg/MJ depending on the type of boiler (automatic or manual), the combustion mode (based on oxygen supply), and the type of fuel. Emissions varied from 0–8 mg/MJ in a modern pellet-fired and automatically-controlled boiler, and from 1–25 mg/MJ in a wood-fired water heating boiler, with the highest emissions found for softwood (spruce). In the pellet stove with automatic feeding and control, BC emissions varied between 1 and 120 mg/MJ, with the highest emissions detected for wood pellets, and in the wood-burning fireplace, the emissions varied between 6 and 80 mg/MJ, with the highest emissions detected for birch firewood. Full article

Tree rings record environmental conditions and can serve as long-term biomonitors of urban pollution. This study evaluated the radial growth and chemical composition of Pinus greggii wood in three urban green areas of Mexico City: San Juan de Aragón Park (SJA), Sierra de Guadalupe State Park (GUAD), and Vivero Coyoacán National Park (COY). Tree ring chemical elements were analyzed at annual resolution for the period 2002 to 2022, and their relationships with atmospheric pollutant concentrations, including nitrogen oxides (NO_x), carbon monoxide (CO), ozone (O₃), and particulate matter (PM), of medium size or smaller than 10 µm, including the fractions PM_2.5 and PM₁₀, were assessed using a spatial scaling approach. Elemental concentrations were determined using X-ray fluorescence (XRF). Statistical analyses included analysis of variance (ANOVA), Theil–Sen trend estimation, and Pearson correlation with lag analysis (up to 3 years). The oldest trees were recorded in COY (52 years), while the youngest were recorded in GUAD (13 years). Distinct temporal patterns in elemental concentrations were detected among sites; for instance, peak concentrations of Fe (307 ppm), Cu (11 ppm), and Zn (51 ppm) occurred in GUAD in 2021, while Pb concentrations declined during 2019–2020 across all three sites. Significant correlations (p < 0.05) were identified between Cu, Fe, Zn, and Pb and the atmospheric pollutants (NO_x, PM_2.5, PM₁₀, O₃). Notably, O₃ showed significant positive correlations with Fe at SJA (up to r = 0.80) and GUAD (up to r = 0.46) with lags ranging from 0 to 3 years, suggesting delayed responses between atmospheric pollution and elemental deposition in tree rings. These findings highlight the sensitivity of P. greggii to urban atmospheric pollution and support its potential as a long-term biomonitoring tool, as well as its importance for informing policies aimed at improving air quality and promoting the sustainable management of urban green spaces. Full article

This study investigated sub-basin variability in dissolved (dBa)–excess particulate (Ba_xs) barium relationships and Ba flux patterns across the western and central Mediterranean Sea during late spring 2017 (PEACETIME cruise). The dBa concentrations increased from ~35 nmol L⁻¹ near the surface to ~70 nmol L⁻¹ at 2500 m, consistent with the relatively weak vertical dBa gradient typical of the Mediterranean. Depth profiles of dBa showed distributions consistent with Ba_xs dynamics associated with organic matter remineralization at mesopelagic depths (100–1000 m). Ba_xs exhibited basin-dependent maxima, with lower (<300 pM) depth-weighted average concentrations confined to the upper mesopelagic in the Tyrrhenian and Ionian basins and higher (up to 650 pM) and deeper concentrations (to ~1000 m) in the Algero–Provençal basin, suggesting contrasted remineralization horizon structures. A simplified steady-state 1-D approach yielded first-order mesopelagic dBa removal fluxes of ~0.3 ± 0.1 µmol m⁻² d⁻¹ in the Algero–Provençal basin to 1.7 ± 1.0 µmol m⁻² d⁻¹ in the Ionian basin, consistent with previous estimates obtained from a coupled dBa and parametric optimum multiparameter approach. Together, these paired dissolved and particulate Ba observations refined the Mediterranean Ba cycle framework and provided additional geochemical constraints for interpreting mesopelagic carbon remineralization processes. Full article

Enhancing soil nutrient content is fundamental to the ecological restoration of degraded soils. The application of microalgae represents a sustainable approach for soil remediation, as it contributes to environmental CO₂ sequestration while recycling nutrients into degraded ecosystems. Through a 105-day laboratory incubation experiment, this study investigated the impact of applying a mixed microalgal suspension containing active/inactive Chlorella ZJ and Anabaena azotica on the C and N fractions of an alkaline, degraded purple soil. The results showed that both active and inactive microalgae treatments (AM and IM) significantly decreased soil pH and increased soil moisture content (SMC). The AM treatment notably increased the proportion of large soil aggregates and enhanced soil structure. Both treatments significantly enhanced soil C and N fractions: dissolved organic carbon/nitrogen (DOC/DON) increased by 6.41/5.81 times (AM) and 4.22/4.76 times (IM) that of the control (without microalgae application); total organic carbon (TOC) rose by 147.07% (AM) and 138.73% (IM); and the contents of coarse particulate and mineral-associated organic C and N were also significantly elevated. Total nitrogen (TN) significantly increased only under the AM treatment. Soil C and N mineralization capacities were enhanced by 1.01–1.34 times and 7.56–8.43 times that of the control, respectively, indicating a more pronounced stimulation of N mineralization. Fluorescence analysis revealed that both AM and IM treatments increased the complexity and humification of dissolved organic matter. The application of microalgae significantly improved the soil structure and chemical characteristics of the degraded soil and enhanced the C/N pools, thereby creating favorable conditions for soil restoration. Full article

The transport sector is a major contributor to urban air pollution and greenhouse gas emissions in Pakistan, posing significant challenges to sustainable development and climate commitments. This study develops the first technology-resolved, high-resolution, multi-pollutant emission inventory and scenario analysis for Pakistan’s transport sector, addressing key gaps in previous studies that lacked integrated multi-pollutant assessments, comprehensive coverage of non-road sources, and long-term scenario comparisons. The analysis integrates road and non-road transport sources within the Greenhouse Gas–Air Pollution Interactions and Synergies (GAINS) modeling framework. Emissions are projected for 2024–2050 under a business-as-usual (BAU) scenario and three mitigation pathways: an Electric Vehicle Transition (EVT) emphasizing transport electrification, a Euro-VI scenario focusing on stringent fuel and vehicle emission standards, and an integrated nationally determined contribution strategy (NDC+) scenario combining electrification, regulatory improvements, and structural transport reforms. In 2024, transport-related emissions are estimated at approximately 22 kt of fine particulate matter (PM_2.5), over 300 kt of nitrogen oxides (NO_x), and nearly 39 Mt of carbon dioxide (CO₂), alongside substantial emissions of other gaseous pollutants and short-lived climate forcers. By 2050, the NDC+ scenario achieves the largest reductions relative to business-as-usual, demonstrating that coordinated electrification and emission control strategies can simultaneously reduce air pollution and greenhouse gas emissions. The results demonstrate strong synergies between climate mitigation and air quality improvement, showing that integrated strategies combining electrification with stringent emission standards can simultaneously reduce greenhouse gas emissions and major air pollutants while advancing cleaner and more sustainable mobility. This analysis provides a consistent and policy-relevant evidence base derived from best-available data and modeling tools to support Pakistan’s NDC implementation, sustainable mobility planning, and integrated air quality and climate strategies, with lessons transferable to other rapidly developing economies. Full article

Traditional road tunnel ventilation systems suffer from high energy consumption and limited effectiveness in fire smoke control. Thus, there is a pressing need to develop advanced air purification technologies that integrate low energy demand with efficient smoke mitigation capabilities. In this study, a self-developed negative ion purification system was implemented, and systematic full-scale experimental investigations were conducted in both a test tunnel and an operational road tunnel to evaluate its performance in air purification and smoke suppression under normal operation and fire conditions. Key parameters, including negative ion concentration, particulate matter concentration, carbon monoxide (CO) concentration, and smoke distribution characteristics, were measured to elucidate smoke evolution behavior and the underlying mechanisms influenced by negative ions. The results show that the negative ion purification system can rapidly establish a high-concentration negative ion field within the tunnel space. Under normal operating conditions, negative ions markedly reduce particulate matter concentrations and their fluctuations, thereby effectively improving tunnel air quality. Under fire conditions, the system maintains high purification efficiency, with significant reductions in particulate matter concentration observed in the test tunnel and clear suppression of longitudinal particulate transport in the real tunnel. In particular, PM10 exhibits a higher removal efficiency. In addition, negative ions promote particle agglomeration and gravitational settling, accelerate CO dilution and dispersion, and significantly improve tunnel visibility. The results demonstrate that the negative ion purification system exhibits strong applicability and considerable engineering potential across different spatial scales and fire scenarios. Full article

Building on prior evidence that biomass cooking drives personal air pollution in rural and peri-urban Bangladesh, we measured kitchen pollution alongside personal exposure and examined the influence of outdoor industrial and traffic emissions on personal and indoor air quality. In an mHealth based-behavior change communication (BCC) intervention study (NCT05570552), 400 women were enrolled from rural Matlab and peri-urban Araihazar in Bangladesh. We measured 24 h personal exposure to fine particulate matter 2.5 (PM_2.5) and black carbon (BC) using personal monitors (UPAS V2), and 72–120 h PM_2.5 in 200 kitchens and outdoors of households using air quality sensors (PurpleAir Flex). Compared to clean fuel users, biomass users showed greater personal and kitchen exposure to PM_2.5, showing good correlation between personal and indoor PM_2.5 measurements (R² = 0.722). Daily average personal PM_2.5 and kitchen PM_2.5 during both cooking and non-cooking periods were higher in rural than peri-urban areas. Geographic information system mapping revealed that personal PM_2.5 was inversely related to the distance of factories from households when below <300 m in both rural and urban areas. Only in Araihazar, personal BC was higher in households located near factories or roads (<200–300 m) compared to those situated further away. Higher personal BC exposure was found in peri-urban women than rural women (p < 0.001). Higher levels of PM_2.5 and increased BC were found in rural and peri-urban households, respectively, which were located in close proximities to formal/informal factories and main roads. These findings highlight the need for sustainable household energy transitions and improved air quality management to reduce air pollution exposure in Bangladesh. Full article

We evaluated the indoor environmental quality of the administrative office at University Medical Center Ho Chi Minh City branch 2 and implemented a multi-stage engineering control strategy to optimize occupational health conditions. A cross-sectional assessment monitored important air quality parameters, including carbon dioxide (CO₂), fine particulate matter (PM2.5 and PM10), humidity, and illumination. Following baseline measurements, an integrated system was deployed to address pollutant mass balance, consisting of High-Efficiency Particulate Air (HEPA) filtration units for mechanical particle scrubbing, ceiling-mounted axial fans to induce forced convection, and ultraviolet-C germicidal lamps for photochemical disinfection. Post-intervention results demonstrated significant gains in system removal efficiency. CO₂ concentrations decreased by over 60% due to enhanced volumetric air exchange, while PM2.5 levels decreased by more than 40% through interception and diffusion mechanisms within the HEPA media. Furthermore, UVC irradiation achieved a 90% reduction in viable airborne microbial colonies. The results of this study show that low-cost, scalable environmental engineering controls and fluid dynamic optimizations effectively mitigate indoor air pollution and enhance workplace stability in healthcare administrative settings. Full article

Pollution remains a major global public health concern increasingly associated with cancer incidence. This systematic review and meta-analyses examined the association between cancer risk and pollution across air, water, and land following the PRISMA guidelines. From 26,367 records initially identified in PubMed, Web of Science, and Scopus (January 2014–June 2025), 168 studies met the eligibility criteria. Meta-analyses conducted on 11 groups of studies revealed significant associations of lung cancer with fine particulate matter (HR_pooled = 1.347; 95% CI: 1.158–1.536), black carbon (HR_pooled = 1.096; 95% CI: 1.014–1.179) and ozone (HR_pooled = 0.941; 95% CI: 0.908–0.975), and breast cancer with nitrogen dioxide (HR_pooled = 1.064; 95% CI: 1.011–1.117). The association of ozone with cancer risks was inconsistent. While 155 studies reported on cancer risks from air pollution, only 10 studies focused on water pollutants and two on land pollutants, primarily heavy metals. Also, 79% of reviewed studies originated from only six high-income countries. The findings suggest that while particulate matter is a consistent risk factor, the global evidence base remains imbalanced based on pollution type and economic status of countries. Addressing these data gaps through targeted research in underrepresented regions and prioritizing the reduction of exposure to identified carcinogenic pollutants could reduce the global cancer burden. Full article

Urban air pollution is increasingly recognised as a determinant of mental wellbeing, yet most existing studies rely on static exposure estimates and lack spatial granularity. This limits understanding of how pollutant-specific patterns influence psychological states in real-world settings. To address this gap, we integrate real-time environmental and physiological data from 40 participants using the DigitalExposome dataset, applying multivariate and spatial analysis techniques. Our findings confirm that Particulate Matter (PM_2.5) exerts the strongest negative association with mental wellbeing while extending prior work by establishing a preliminary ranking of other pollutants Particulate Matter (PM₁₀), Particulate Matter (PM₁), Carbon Monoxide (CO), Nitrogen Dioxide (NO₂), Ammonia (NH₃). We applied statistical and spatial analysis methods, including heatmaps and Voronoi diagrams, to explore links between pollutants and wellbeing and compare the relative influence of air pollution and noise. This enabled identification of pollutant-specific hotspots and multi-level wellbeing patterns across individual, accumulated, and collective scales. These results demonstrate the value of spatial analysis for environmental health research and support targeted urban interventions, such as green space placement and traffic re-routing, to mitigate mental wellbeing risks. Full article

This study investigates the vertical variation and temporal characteristics and indicates the sources of black carbon (BC) and brown carbon (BrC) within particulate matter fraction PM₁ during light (November–December 2024) and heavy (January–February 2025) haze episodes in Bangkok, Thailand, a topic where data are still limited data regarding Southeast Asian megacities. Continuous measurements were conducted at 30 and 110 m above ground level, together with particle size distribution measurement, micrometeorological observations, and backward air mass trajectory analysis. During the haze periods, the highest particle number concentrations occurred in the 0.3–0.4 µm size range, indicating dominant contributions from combustion-related emissions and secondary aerosol formation. Mean PM₁ mass concentrations during the heavy haze episodes were more than 2.5 times higher than those during light haze. BC concentrations increased substantially during heavy haze, while the BC fraction of PM₁ remained relatively constant (~10%). In contrast, the BrC fraction reached nearly 20%, reflecting an increasing influence of biomass burning emissions associated with regional transport. Combined analyses of BC/BrC relationships, wind-direction dependence, and air mass trajectories demonstrate mixed contributions from local fossil fuel combustion and long-range transport of biomass burning aerosols during severe haze events. Full article

Crop residue open burning is a major source of atmospheric pollutants that degrade regional air quality, enhance climate forcing, and threaten public health through emissions of particulate matter, greenhouse gases, and toxic species. In southern China, satellite-based emission estimates are often underestimated because frequent cloud cover and limited spatiotemporal resolution hinder the detection of agricultural fires. In this study, crop residue open burning emissions in Guangxi province from 2017 to 2023 were quantified using a statistical approach. The open burning proportion (OBP) was updated on an annual basis using the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG), and recently reported emission factors (EF_S) were adopted to enhance estimation accuracy. Annual emissions of pollutants were then spatially distributed to 0.05° × 0.05° grid cells based on satellite-detected fire counts and land cover information. The results indicated the total emissions of black carbon (BC), organic carbon (OC), sulfur dioxide (SO₂), nitric oxide (NO_X), carbon monoxide (CO), carbon dioxide (CO₂), fine particles (PM_2.5), coarse particles (PM₁₀), ammonia (NH₃), methane (CH₄) and non-methane volatile organic compound (NMVOC) in Guangxi province during 2017–2023 were 58.90, 230.48, 37.90, 213.95, 4234.41, 108,775.48, 583.09, 667.70, 46.36, 322.74 and 710.20 Gg, respectively. Sugarcane residue burning was identified as the dominant contributor, accounting for 41.26–64.38% of total emissions, followed by rice (20.66–43.06%), corn (5.11–17.25%), and cassava (4.33–6.45%). Emissions exhibited clear interannual variability, declining from 2017 to 2020 under strict control measures and increasing again from 2021 to 2023 as enforcement weakened. Incorporating annually updated VIIRS-derived OBP_S into the statistical inventory improves the temporal representation and reliability of multi-year emission estimates for agricultural burning. Full article

This paper presents the design of a sensor system for remote measurements of exhaust emissions from automotive combustion engines. The system’s purpose is to determine the likelihood of a given vehicle’s potential harmfulness to the environment. This system, if implemented, could detect vehicles posing a threat to the environment in road traffic. A remote measurement system can be installed in the front of a measuring vehicle driving behind the vehicle being diagnosed. This approach allows for rapid road testing of multiple vehicles while they are operating in real-world conditions where engines can emit the highest levels of undesirable pollutants. Exceeding emission standards may be related to modifications made to the vehicle’s exhaust gas aftertreatment systems, engine wear, or malfunctions of engine-related systems such as the diesel particulate filter (DPF) or catalytic converter. Toxic and undesirable substances include carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NO_x), carbon dioxide (${\mathrm{C}\mathrm{O}}_2$), and particulate matter (PM) particles. The main goal of the measurements is to identify vehicles that potentially pose a threat to the environment during normal operation. The sensor system consists of several types of sensors utilizing various physical and chemical phenomena, with particular emphasis on their low cost and easy availability. The measurement unit utilizes MEMS technology, photoacoustic spectroscopy, electrochemical methods, light absorption and scattering, spectrophotometry, and electro-optical detection. Full article

Chemical coagulation is a highly important step of the conventional treatment processes, determination of the optimum coagulant dose to meet the demand of particulate materials and natural organic matters (NOMs) in raw water is crucial for good drinking water quality. WTC-Coag is a universal non-site-specific coagulant prediction model using three raw water quality parameters, UV₂₅₄, colour, and turbidity, as model inputs. The empirical model can determine the dose for maximum dissolved organic carbon (DOC) removal to achieve the conditions of enhanced coagulation; it also features an operator-selectable input—% setpoint (as % DOC removal)—to establish a dose for the desirable treated water quality. This hybrid modelling and control approach in practice is extremely useful for operators to be able to optimise the process by balancing between water quality and use of resources (chemical and sludge disposal costs) for sustainable operation. This paper discusses the practicality of this hybrid modelling approach via a long-term evaluation by comparing the plant dose against predicted dose using five years historical operations and water quality data. The assessment covered raw water quality change against treatment performance, predictability, usability and operator behaviour in response to the dose change situation. During the study period, five “black water” events were captured, and the performance of the predictability due to operational changes and operator’s response in these extreme events have been analysed. The comparison between the predicted enhanced dose and the plant dose indicated enhanced coagulation would not be always required. Furthermore, the selection of 50% setpoint from the targeted dose option matched well with the plant dose during which the lower-dose situation would be sufficient, with 90% of the predicted doses within ±10 mg/L of the plant dose and 95% of the predicted doses within ±15 mg/L of the plant dose during the normal period. The use of a correction factor to compensate for the particulate demand due to powdered activated carbon (PAC) dose during “black water” events has shown to be effective. The 50% setpoint matches with the plant alum dose over the entire period after accounting for the PAC dose, with 70% of the predicted doses within ±10 mg/L and 80% within ±15 mg/L of the plant dose. All the coagulation-related prediction functions have been evaluated and confirmed their non-site-specific nature. This study is unique in terms of using real operations data for an extended period to evaluate this novel hybrid modelling concept towards the sustainability goal. Full article