Within-city spatial variations in PM2.5 magnetite nanoparticles and brain cancer incidence in Toronto and Montreal, Canada.

Magnetite nanoparticles are small, strongly magnetic iron oxide particles which are produced during high-temperature combustion and friction processes and form part of the outdoor air pollution mixture. These particles can translocate to the brain and have been found in human brain tissue. In this study, we estimated associations between within-city spatial variations in concentrations of magnetite nanoparticles in outdoor fine particulate matter (PM2.5) and brain cancer incidence. We performed a cohort study of 1.29 million participants in four cycles of the Canadian Census Health and Environment Cohort in Montreal and Toronto, Canada who were followed for malignant brain tumour (glioma) incidence. As a proxy for magnetite nanoparticle content, we measured the susceptibility of anhysteretic remanent magnetization (χARM) in PM2.5 samples (N = 124 in Montreal, N = 110 in Toronto), and values were assigned to residential locations. Stratified Cox proportional hazards models were used to estimate hazard ratios (per IQR change in volume-normalized χARM). ARM was not associated with brain tumour incidence (HR = 0.998, 95% CI 0.988, 1.009) after adjusting for relevant potential confounders. Although we found no evidence of an important relationship between within-city spatial variations in airborne magnetite nanoparticles and brain tumour incidence, further research is needed to evaluate this understudied exposure, and other measures of exposure to magnetite nanoparticles should be considered.

Spatialtemporal evolution characteristics of ozone in China and its response to urbanization.

Based on the background of urbanization in China, we used the dynamic spatial panel Durbin model to study the driving mechanism of ozone pollution empirically. We also analyzed the spatial distribution of ozone driving factors using the GTWR. The results show that: i) The average annual increase of ozone concentration in ambient air in China from 2015 to 2019 was 1.68µg/m3, and 8.39µg/m3 elevated the year 2019 compared with 2015. ii) The Moran's I value of ozone in ambient air was 0.027 in 2015 and 0.209 in 2019, showing the spatial distribution characteristics of "east heavy and west light" and "south low and north high". iii) Per capita GDP industrial structure, population density, land expansion, and urbanization rate have significant spillover effects on ozone concentration, and the regional spillover effect is greater than the local effect. R&D intensity and education level have a significant negative impact on ozone concentration. iv) There is a decreasing trend in the inhibitory effect of educational attainment and R&D intensity on ozone concentration, and an increasing trend in the promotional effect of population urbanization rate, land expansion, and economic development on ozone concentration. Empirical results suggest a twofold policy meaning: i) to explore the causes behind the distribution of ozone from the new perspective of urbanization, and to further the atmospheric environmental protection system and ii) to eliminate the adverse impacts of ozone pollution on nature and harmonious social development.

The comprehensive impact of thermal-PM2.5 interaction on subjective evaluation of urban outdoor space: A pilot study in a cold region of China.

Urban outdoor space has a very important impact on the quality of people's outdoor activities, which has influenced people's health and moods. Its influence is the result of the combined action of various factors. Thermal and air quality environment are important factors affecting the overall comfort of the urban outdoor space. At present, there are few research on interaction with thermal and air quality environment. Therefore, a meteorological measurement and questionnaire survey have been conducted in a representative open space in a campus in Xi'an, China. The following are the research results:(1) Mean physiological equivalent temperature (MPET) is a significant factor affecting thermal sensation vote (TSV) and thermal comfort vote (TCV). PM2.5 has no significant effect on thermal comfort vote (TCV), but it is a considerable factor affecting thermal sensation vote (TSV) when 10.2°C ≤ MPET<21°C (P = 0.023 *). (2) PM2.5 is a significant factor affecting air quality vote (AQV) and breathing comfort vote (BCV).Mean physiological equivalent temperature (MPET) has no significant impact on air quality vote (AQV), but it is a considerable factor affecting breathing comfort vote (BCV) when 10.2°C ≤ MPET<21°C (P = 0.01 **). (3) Mean physiological equivalent temperature (MPET) is a significant factor affecting overall comfort vote (OCV), but PM2.5 is not. In general, When 10.2°C ≤ MPET<21°C (-0.5 < -0.37 ≤ TCV ≤ 0.12 <0.5), the interaction between thermal and PM2.5 environment is significant on thermal sensation vote (TSV) and breathing comfort vote (BCV). This study can provide experimental support for the field of multi-factor interaction, which has shown that improving the thermal environment can better breathing comfort, while reducing PM2.5 concentration can promote thermal comfort. And can also provide reference for the study of human subjective comfort in urban outdoor space in the same latitude of the world.

Association between synoptic types in Beijing and acute myocardial infarction hospitalizations: A comprehensive analysis of environmental factors.

BACKGROUND: Environmental factors like air pollution and temperature can trigger acute myocardial infarction (AMI). However, the link between large-scale weather patterns (synoptic types) and AMI admissions has not been extensively studied. This research aimed to identify the different synoptic air types in Beijing and investigate their association with AMI occurrences. METHODS: We analyzed data from Beijing between 2013 and 2019, encompassing 2556 days and 149,632 AMI cases. Using principal component analysis and hierarchical clustering, classification into distinct synoptic types was conducted based on weather and pollution measurements. To assess the impact of each type on AMI risk over 14 days, we employed a distributed lag non-linear model (DLNM), with the reference being the lowest risk type (Type 2). RESULTS: Four synoptic types were identified: Type 1 with warm, humid weather; Type 2 with warm temperatures, low humidity, and long sunshine duration; Type 3 with cold weather and heavy air pollution; and Type 4 with cold temperatures, dryness, and high wind speed. Type 4 exhibited the greatest cumulative relative risk (CRR) of 1.241 (95%CI: 1.150, 1.339) over 14 days. Significant effects of Types 1, 3, and 4 on AMI events were observed at varying lags: 4-12 days for Type 1, 1-6 days for Type 3, and 1-11 days for Type 4. Females were more susceptible to Types 1 and 3, while individuals younger than 65 years old showed increased vulnerability to Types 3 and 4. CONCLUSION: Among the four synoptic types identified in Beijing from 2013 to 2019, Type 4 (cold, dry, and windy) presented the highest risk for AMI hospitalizations. This risk was particularly pronounced for males and people under 65. Our findings collectively highlight the need for improved methods to identify synoptic types. Additionally, developing a warning system based on these synoptic conditions could be crucial for prevention.

Meta-analysis of fine particulate matter exposure during pregnancy and birth weight: Exploring sources of heterogeneity.

BACKGROUND: Several meta-analyses assessed the relationship between exposure to PM with aerodynamic diameter ≤ 2.5 µm (PM2.5) during pregnancy and birth weight (BW), but results were inconsistent and substantial unexplained heterogeneity was reported. We aimed to investigate the above association and to explore sources of heterogeneity across studies. METHODS: We systematically reviewed the current worldwide evidence examining the association between PM2.5 and BW. The review protocol was registered on the PROSPERO website (CRD42020188996) and followed PRISMA guidelines. We extracted association measures for BW and low birth weight (LBW, BW < 2500 g) from each study to evaluate pooled summary measures and to explore sources of between-study heterogeneity. FINDINGS: Of the 2677 articles identified, 84 met the inclusion criteria (~42 M births). Our random effects meta-analyses revealed substantial heterogeneity among included studies (I2 = 98.4 % and I2 = 77.7 %, for BW and LBW respectively). For LBW, the heterogeneity decreased (I2 = 59.7 %) after excluding four outlying studies, with a pooled odds ratio 1.07 (95 % confidence interval, CI: 1.05, 1.09) per a 10-µg/m3 increase in mean PM2.5 exposure over the entire pregnancy. Further subgroup analysis revealed geographic heterogeneity with higher association in Europe (1.34, (1.16, 1.55)) compared to Asia (1.06, (1.03, 1.10)) and US (1.07, (1.04, 1.10)). CONCLUSION: The association between PM2.5 and birth weight varied depending on several factors. The sources of heterogeneity between studies included modifiers such as study region and period. Hence, it is advisable not to pool summary measures of PM2.5-BW associations and that policy would be informed by local evidence.

Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables.

Concerns about methane (CH4) emissions from rice, a staple sustaining over 3.5 billion people globally, are heightened due to its status as the second-largest contributor to greenhouse gases, driving climate change. Accurate quantification of CH4 emissions from rice fields is crucial for understanding gas concentrations. Leveraging technological advancements, we present a groundbreaking solution that integrates machine learning and remote sensing data, challenging traditional closed chamber methods. To achieve this, our methodology involves extensive data collection using drones equipped with a Micasense Altum camera and ground sensors, effectively reducing reliance on labor-intensive and costly field sampling. In this experimental project, our research delves into the intricate relationship between environmental variables, such as soil conditions and weather patterns, and CH4 emissions. We achieved remarkable results by utilizing unmanned aerial vehicles (UAV) and evaluating over 20 regression models, emphasizing an R2 value of 0.98 and 0.95 for the training and testing data, respectively. This outcome designates the random forest regressor as the most suitable model with superior predictive capabilities. Notably, phosphorus, GRVI median, and cumulative soil and water temperature emerged as the model's fittest variables for predicting these values. Our findings underscore an innovative, cost-effective, and efficient alternative for quantifying CH4 emissions, marking a significant advancement in the technology-driven approach to evaluating rice growth parameters and vegetation indices, providing valuable insights for advancing gas emissions studies in rice paddies.

Influence of meteorological variables and air pollutants on fog/smog formation in seven major cities of Indo-Gangetic Plain.

The Indo-Gangetic Plains (IGP) of the Indian subcontinent during winters experience widespread fog episodes. The low visibility is not only attributed to meteorological conditions but also to the increased pollution levels in the region. The study was carried out for Tier 1 and Tier II cities of the IGP of India, including Kolkata, Amritsar, Patiala, Hisar, Delhi, Patna, and Lucknow. This work analyzes data from 1990 to 2023 (33 years) employing the Mann-Kendall-Theil-Sen slope to determine the trends in fog occurrences and the relation between fog and meteorological parameters using multiple linear regressions. Furthermore, identifying the most relevant fog (visibility)-impacting factors from a set of both meteorological factors and air pollutants using step-wise regression. All cities indicated trend in the number of foggy days except for Kolkata. The multiple regression analysis reveals relatively low associations between fog occurrences and meteorological factors (30 to 59%), although the association was stronger when air pollution levels were considered (60 to 91%). Relative humidity, PM2.5, and PM10 have the most influence on fog formation. The study provides comprehensive insights into fog trends by incorporating meteorological data and air pollution analysis. The findings highlight the significance of acknowledging meteorological and pollution factors to understand and mitigate the impacts of reduced visibility. Hence, this information can guide policymakers, urban planners, and environmental management agencies in developing effective strategies to manage fog-related risks and improve air quality.

Atmospheric gaseous mercury and associated health risk assessment in the economic capital of India.

Mercury cycling in coastal metropolitan areas on the west coast of India becomes complex due to the combined effects of both intensive domestic anthropogenic emissions and marine air masses. The present study is based on yearlong data of continuous measurements of gaseous elemental mercury (GEM) concentration concurrent with meteorological parameters and some air pollutants at a coastal urban site in Mumbai, on the west coast of India, for the first time. The concentration of GEM was found in a range between 2.2 and 12.3 ng/m3, with a mean of 3.1 ± 1.1 ng/m3, which was significantly higher than the continental background values in the Northern Hemisphere (~ 1.5 ng/m3). Unlike particulates, GEM starts increasing post-winter to peak during the monsoon and decrease towards winter. July had the highest concentration of GEM followed by October, and a minimum in January. GEM exhibited a distinct diurnal cycle, mainly with a broad peak in the early morning, a narrow one by nightfall, and a minimum in the afternoon. The peaks and their timing suggest the origin of urban mobility and the start of local activities. A positive correlation between SO2, PM2.5, temperature, relative humidity, and GEM indicates that emissions from local industrial plants in the Mumbai coastal area. Principal component analysis (PCA) and cluster analysis (CA) confirm this fact. Monthly back trajectory analysis showed that air mass flows are predominantly from the Arabian Sea and local human activities. Assessment of human health risks by USEPA model reveals that the hazardous quotient, HQ < 1, implies negligible carcinogenic risk. GEM observations in Mumbai during the study period are below the World Health Organization's (WHO) safe limit (200 ng/m3) for long-term inhalation.

Adopting electric school buses in the United States: Health and climate benefits.

Electric school buses have been proposed as an alternative to reduce the health and climate impacts of the current U.S. school bus fleet, of which a substantial share are highly polluting old diesel vehicles. However, the climate and health benefits of electric school buses are not well known. As they are substantially more costly than diesel buses, assessing their benefits is needed to inform policy decisions. We assess the health benefits of electric school buses in the United States from reduced adult mortality and childhood asthma onset risks due to exposure to ambient fine particulate matter (PM2.5). We also evaluate climate benefits from reduced greenhouse-gas emissions. We find that replacing the average diesel bus in the U.S. fleet in 2017 with an electric bus yields $84,200 in total benefits. Climate benefits amount to $40,400/bus, whereas health benefits amount to $43,800/bus due to 4.42*10-3 fewer PM2.5-attributable deaths ($40,000 of total) and 7.42*10-3 fewer PM2.5-attributable new childhood asthma cases ($3,700 of total). However, health benefits of electric buses vary substantially by driving location and model year (MY) of the diesel buses they replace. Replacing old, MY 2005 diesel buses in large cities yields $207,200/bus in health benefits and is likely cost-beneficial, although other policies that accelerate fleet turnover in these areas deserve consideration. Electric school buses driven in rural areas achieve small health benefits from reduced exposure to ambient PM2.5. Further research assessing benefits of reduced exposure to in-cabin air pollution among children riding buses would be valuable to inform policy decisions.

Estimated greenhouse gas emissions in the Mbalmayo thermal power plant between 2016-2020 using the genetic-Gaussian algorithm coupling.

The greenhouse gas (GHG) emissions inventories in our context result from the production of electricity from fuel oil at the Mbalmayo thermal power plant between 2016 and 2020. Our study area is located in the Central Cameroon region. The empirical method of the second level of industrialisation was applied to estimate GHG emissions and the application of the genetic algorithm-Gaussian (GA-Gaussian) coupling method was used to optimise the estimation of GHG emissions. Our work is of an experimental nature and aims to estimate the quantities of GHG produced by the Mbalmayo thermal power plant during its operation. The search for the best objective function using genetic algorithms is designed to bring us closer to the best concentration, and the Gaussian model is used to estimate the concentration level. The results obtained show that the average monthly emissions in kilograms (kg) of GHGs from the Mbalmayo thermal power plant are: 526 kg for carbon dioxide (CO2), 971.41 kg for methane (CH4) and 309.41 kg for nitrous oxide (N2O), for an average monthly production of 6058.12 kWh of energy. Evaluation of the stack height shows that increasing the stack height helps to reduce local GHG concentrations. According to the Cameroonian standards published in 2021, the limit concentrations of GHGs remain below 30 mg/m3 for CO2 and 200 µg/m3 for N2O, while for CH4 we reach the limit value of 60 µg/m3. These results will enable the authorities to take appropriate measures to reduce GHG concentrations.

Continued Rise in Health Burden from Ambient PM2.5 in India under SSP Scenarios Until 2100 despite Decreasing Concentrations.

Forecasting alterations in ambient air pollution and the consequent health implications is crucial for safeguarding public health, advancing environmental sustainability, informing economic decision making, and promoting appropriate policy and regulatory action. However, predicting such changes poses a substantial challenge, requiring accurate data, sophisticated modeling methodologies, and a meticulous evaluation of multiple drivers. In this study, we calculate premature deaths due to ambient fine particulate matter (PM2.5) exposure in India from the 2020s (2016-2020) to the 2100s (2095-2100) under four different socioeconomic and climate scenarios (SSPs) based on four CMIP6 models. PM2.5 concentrations decreased in all SSP scenarios except for SSP3-7.0, with the lowest concentration observed in SSP1-2.6. The results indicate an upward trend in the five-year average number of deaths across all scenarios, ranging from 1.01 million in the 2020s to 4.12-5.44 million in the 2100s. Further analysis revealed that the benefits of reducing PM2.5 concentrations under all scenarios are largely mitigated by population aging and growth. These findings underscore the importance of proactive measures and an integrated approach in India to improve atmospheric quality and reduce vulnerability to aging under changing climate conditions.

Characteristics of PM2.5 bounded carbonaceous aerosols, carbon dioxide and its stable carbon isotopes (δ13C) in rural households in northwest China: Effect of different fuel combustion.

In order to fully understand the carbon emission from different fuels in rural villages of China, especially in the typical atmospheric pollution areas. The characteristics of carbonaceous aerosols and carbon dioxide (CO2) with its stable carbon isotope (δ13C) were investigated in six households, which two households used coal, two households used wood as well as two households used biogas and liquefied petroleum gas (LPG), from two rural villages in Fenwei Plain from March to April 2021. It showed that the fine particulate matter (PM2.5) emitted from biogas and LPG couldn't be as lower as expected in this area. However, the clean fuels could relatively reduce the emissions of organic carbon (OC) and element carbon (EC) in PM2.5 compare to the solid fuels. The pyrolyzed carbon (OP) accounted more total carbon (TC) in coal than the other fuels use households, indicating that more water-soluble OC existed, and it still had the highest secondary organic carbon (SOC) than the other fuels. Meantime, the coal combustions in the two villages had the highest CO2 concentration of 527.6 ppm and 1120.6 ppm, respectively, while the clean fuels could effectively reduce it. The average δ13C values (-26.9‰) was much lighter than almost all the outdoor monitoring and similar to the δ13C values for coal combustion and vehicle emission, showing that they might be the main contributors of the regional atmospheric aerosol in this area. During the sandstorm, the indoor PM2.5 mass and CO2 were increasing obviously. The indoor cancer risk of PAHs for adults and children were greater than 1 × 10-6, exert a potential carcinogenic risk to human of solid fuels combustion in rural northern China. It is important to continue concern the solid fuel combustion and its health impact in rural areas.

Evaluation of particle and volatile organic compound emissions during the use of 3D pens.

This study investigated particle and volatile organic compound (VOC) emission rates (ER) from 3D pens, which are increasingly popular in children's toys. Nine filaments and two 3D pens were evaluated using a flow tunnel, a scanning mobility particle sizer, a proton-transfer-reaction time-of-flight mass spectrometer for particles, and a thermal desorption-gas chromatography-mass spectrometer for VOCs. Results showed that the ERs varied with the pen type, filament, and brand. The particle ER was highest for acrylonitrile butadiene styrene (ABS), followed by polylactic acid (PLA) and polycaprolactone (PCL). Notably, ERs of 83 % and 33 % of ABS and PLA filaments exceeded the maximum allowable particle ER (MAER; 5 × 109 particles/min) for 3D printers but were lower than the VOC MAER (173 µg/min in the office). Different filaments emitted diverse VOCs; ABS emitted styrene and benzene, PLA emitted lactide, and PCL emitted phenol. While particle ERs from 3D pens were comparable to those from printers, the total VOC ERs from 3D pens were slightly lower. Caution is warranted when using 3D pens because of potential health risks, especially their prolonged use, proximity to the breathing zone, and usage by children. This study highlights the need for considering particles and VOCs when assessing the safety of 3D pens, emphasizing awareness of potential hazards, particularly in child-oriented settings.

Experimental assessment of pollutant emissions from residential fuel cells and comparative benchmark analysis.

Energy transition currently brings focus on fuel cell micro-combined heat and power (mCHP) systems for residential uses. The two main technologies already commercialized are the Proton Exchange Membrane Fuel Cells (PEMFCs) and Solid Oxide Fuel Cells (SOFCs). The pollutant emissions of one system of each technology have been tested with a portable probe both in laboratory and field-test configurations. In this paper, the nitrogen oxides (NOx), sulphur dioxide (SO2), and carbon monoxide (CO) emission levels are compared to other combustion technologies such as a recent Euro 6 diesel automotive vehicle, a classical gas condensing boiler, and a gas absorption heat pump. At last, a method of converting the concentration of pollutants (in ppm) measured by the sensors into pollutant intensity per unit of energy (in mg/kWh) is documented and reported. This allows for comparing the pollutant emissions levels with relevant literature, especially other studies conducted with other measuring sensors. Both tested residential fuel cell technologies fed by natural gas can be considered clean regarding SO2 and NOx emissions. The CO emissions can be considered quite low for the tested SOFC and even nil for the tested PEMFC. The biggest issue of natural gas fuel cell technologies still lies in the carbon dioxide (CO2) emissions associated with the fossil fuel they consume. The gas absorption heat pump however shows worse NOx and CO levels than the classical gas condensing boiler. At last, this study illustrates that the high level of hybridization between a fuel cell and a gas boiler may be responsible for unexpected ON/OFF cycling behaviours and therefore prevent both sub-systems from operating as optimally and reliably as they would have as standalone units.

Greenhouse gas mitigation and soil carbon stabilization potential of forest biochar varied with biochar type and characteristics.

Biochar is increasingly used in climate-smart agriculture, yet its impact on greenhouse gas (GHG) emissions and soil carbon (C) sequestration remains poorly understood. This study examined biochar-mediated changes in soil properties and their contribution to C stabilization and GHG mitigation by evaluating four types of biochar. Soil carbon dioxide (CO2) and nitrous oxide (N2O) emissions, soil chemical and biological properties, and soil organic carbon (SOC) mineralization kinetics were monitored using greenhouse, laboratory, and modeling experiments. Three pine wood biochars pyrolyzed at 460 °C (PB-460), 500 °C (PB-500), 700 °C (PB-700), and one pine bark biochar from gasification at 760 °C (GB-760) were added into soil at 1 % w/w basis. Soils amended with biochar were used to cultivate sorghum for three months in a greenhouse, followed by three months of laboratory incubation. Data obtained from laboratory incubation was modeled using various statistical approaches. The PB-500 and PB-700 reduced cumulative N2O-N emissions by 68.5 % and 73.9 % and CO2 equivalent C emissions by 66.9 % and 72.4 %, respectively, compared to unamended control. The N2O emissions were positively associated with soil nitrate N, available P, and biochar ash content while negatively associated with SOC. The CO2 emission was negatively related to biochar C:N ratio and volatile matter content. Biochar amended soils had 49.2 % (PB-500) to 87.7 % (PB-700) greater SOC and 22.9 % (PB-700) to 48.1 % (GB-760) greater sorghum yield than the control. While PB-700 had more saprophytes than the control, the GB-760 yielded a greater yield than biochars prepared by pyrolysis. Microbial biomass C was 7.23 to 23.3 % greater in biochar amended soils than in control. The double exponential decay model best explained the dynamics of C mineralization, which was associated with initial soil nitrate N and available P positively and total fungi and protozoa biomass negatively. Biochar amendment could be a climate smart agricultural strategy. Pyrolysis pine wood biochar showed the greatest potential to reduce GHG emissions and enhance SOC storage and stability, and gasification biochar contributed more to SOC storage and increased crop yield.

China's "coal-to-gas" policy had large impact on PM1.0 distribution during 2016-2019.

Particulate matter with an aerodynamic diameter of less than 1 µm (PM1.0) can be extremely hazardous to human health, so it is imperative to accurately estimate the spatial and temporal distribution of PM1.0 and analyze the impact of related policies on it. In this study, a stacking generalization model was trained based on aerosol optical depth (AOD) data from satellite observations, combined with related data affecting aerosol concentration such as meteorological data and geographic data. Using this model, the PM1.0 concentration distribution in China during 2016-2019 was estimated, and verified by comparison with ground-based stations. The coefficient of determination (R2) of the model is 0.94, and the root-mean-square error (RMSE) is 8.49 µg/m3, mean absolute error (MAE) is 4.10 µg/m3, proving that the model has a very high performance. Based on the model, this study analyzed the PM1.0 concentration changes during the heating period (November and December) in the regions where the "coal-to-gas" policy was implemented in China, and found that the proposed "coal-to-gas" policy did reduce the PM1.0 concentration in the implemented regions. However, the lack of natural gas due to the unreasonable deployment of the policy in the early stage caused the increase of PM1.0 concentration. This study can provide a reference for the next step of urban air pollution policy development.

Impacts assessment of nitrification inhibitors on U.S. agricultural emissions of reactive nitrogen gases.

Fertilizer-intensive agriculture leads to emissions of reactive nitrogen (Nr), posing threats to climate via nitrous oxide (N2O) and to air quality and human health via nitric oxide (NO) and ammonia (NH3) that form ozone and particulate matter (PM) downwind. Adding nitrification inhibitors (NIs) to fertilizers can mitigate N2O and NO emissions but may stimulate NH3 emissions. Quantifying the net effects of these trade-offs requires spatially resolving changes in emissions and associated impacts. We introduce an assessment framework to quantify such trade-off effects. It deploys an agroecosystem model with enhanced capabilities to predict emissions of Nr with or without the use of NIs, and a social cost of greenhouse gas to monetize the impacts of N2O on climate. The framework also incorporates reduced-complexity air quality and health models to monetize associated impacts of NO and NH3 emissions on human health downwind via ozone and PM. Evaluation of our model against available field measurements showed that it captured the direction of emission changes but underestimated reductions in N2O and overestimated increases in NH3 emissions. The model estimated that, averaged over applicable U.S. agricultural soils, NIs could reduce N2O and NO emissions by an average of 11% and 16%, respectively, while stimulating NH3 emissions by 87%. Impacts are largest in regions with moderate soil temperatures and occur mostly within two to three months of N fertilizer and NI application. An alternative estimate of NI-induced emission changes was obtained by multiplying the baseline emissions from the agroecosystem model by the reported relative changes in Nr emissions suggested from a global meta-analysis: -44% for N2O, -24% for NO and +20% for NH3. Monetized assessments indicate that on an annual scale, NI-induced harms from increased NH3 emissions outweigh (8.5-33.8 times) the benefits of reducing NO and N2O emissions in all agricultural regions, according to model-based estimates. Even under meta-analysis-based estimates, NI-induced damages exceed benefits by a factor of 1.1-4. Our study highlights the importance of considering multiple pollutants when assessing NIs, and underscores the need to mitigate NH3 emissions. Further field studies are needed to evaluate the robustness of multi-pollutant assessments.

Associations of Long-Term Exposure to Fine Particulate Constituents With Cardiovascular Diseases and Underlying Metabolic Mediations: A Prospective Population-Based Cohort in Southwest China.

BACKGROUND: The health effects of particulate matter with an aerodynamic diameter ≤2.5 µm (PM2.5) might differ depending on compositional variations. Little is known about the joint effect of PM2.5 constituents on metabolic syndrome and cardiovascular disease (CVD). This study aims to evaluate the combined associations of PM2.5 components with CVD, identify the most detrimental constituent, and further quantify the mediation effect of metabolic syndrome. METHODS AND RESULTS: A total of 14 427 adults were included in a cohort study in Sichuan, China, and were followed to obtain the diagnosis of CVD until 2021. Metabolic syndrome was defined by the simultaneous occurrence of multiple metabolic disorders measured at baseline. The concentrations of PM2.5 chemical constituents within a 1-km2 grid were derived based on satellite- and ground-based detection methods. Cox proportional hazard models showed that black carbon, organic matter (OM), nitrate, ammonium, chloride, and sulfate were positively associated with CVD risks, with hazard ratios (HRs) ranging from 1.24 to 2.11 (all P<0.05). Quantile g-computation showed positive associations with 4 types of CVD risks (HRs ranging from 1.48 to 2.25, all P<0.05). OM and chloride had maximum weights for CVD risks. Causal mediation analysis showed that the positive association of OM with total CVD was mediated by metabolic syndrome, with a mediation proportion of 1.3% (all P<0.05). CONCLUSIONS: Long-term exposure to PM2.5 chemical constituents is positively associated with CVD risks. OM and chloride appear to play the most responsible role in the positive associations between PM2.5 and CVD. OM is probably associated with CVD through metabolic-related pathways.

Air pollution, life's essential 8, and risk of severe non-alcoholic fatty liver disease among individuals with type 2 diabetes.

BACKGROUND: The impacts of long-term exposure to air pollution on the risk of subsequent non-alcoholic fatty liver disease (NAFLD) among participants with type 2 diabetes (T2D) is ambiguous. The modifying role of Life's Essential 8 (LE8) remains unknown. METHODS: This study included 23,129 participants with T2D at baseline from the UK Biobank. Annual means of nitrogen dioxide (NO2), nitrogen oxides (NOX), and particulate matter (PM2.5, PM2.5-10, PM10) were estimated using the land-use regression model for each participant. The associations between exposure to air pollution and the risk of severe NAFLD were evaluated using Cox proportional hazard models. The effect modification of LE8 was assessed through stratified analyses. RESULTS: During a median 13.6 years of follow-up, a total of 1,123 severe NAFLD cases occurred. After fully adjusting for potential covariates, higher levels of PM2.5 (hazard ratio [HR] = 1.12, 95%CI:1.02, 1.23 per interquartile range [IQR] increment), NO2 (HR = 1.15, 95%CI:1.04, 1.27), and NOX (HR = 1.08, 95%CI:1.01, 1.17) were associated with an elevated risk of severe NAFLD. In addition, LE8 score was negatively associated with the risk of NAFLD (HR = 0.97, 95% CI: 0.97, 0.98 per point increment). Compared with those who had low air pollution and high LE8, participants with a high air pollution exposure and low LE8 had a significantly higher risk of severe NAFLD. CONCLUSIONS: Our findings suggest that long-term exposure to air pollution was associated with an elevated risk of severe NAFLD among participants with T2D. A lower LE8 may increase the adverse impacts of air pollution on NAFLD.