Degradation of gaseous hydrocarbons in aerated stirred bioreactors inoculated with Rhodococcus erythropolis: Effect of the carbon source and SIFT-MS method development.

The study of microbial hydrocarbons removal is of great importance for the development of future bioremediation strategies. In this study, we evaluated the removal of a gaseous mixture containing toluene, m-xylene, ethylbenzene, cyclohexane, butane, pentane, hexane and heptane in aerated stirred bioreactors inoculated with Rhodococcus erythropolis and operated under non-sterile conditions. For the real-time measurement of hydrocarbons, a novel systematic approach was implemented using Selected-Ion Flow Tube Mass Spectrometry (SIFT-MS). The effect of the carbon source (∼9.5 ppmv) on (i) the bioreactors' performance (BR1: dosed with only cyclohexane as a single hydrocarbon versus BR2: dosed with a mixture of the 8 hydrocarbons) and (ii) the evolution of microbial communities over time were investigated. The results showed that cyclohexane reached a maximum removal efficiency (RE) of 53% ± 4% in BR1. In BR2, almost complete removal of toluene, m-xylene and ethylbenzene, being the most water-soluble and easy-to-degrade carbon sources, was observed. REs below 32% were obtained for the remaining compounds. By exposing the microbial consortium to only the five most recalcitrant hydrocarbons, REs between 45% ± 5% and 98% ± 1% were reached. In addition, we observed that airborne microorganisms populated the bioreactors and that the type of carbon source influenced the microbial communities developed. The abundance of species belonging to the genus Rhodococcus was below 10% in all bioreactors at the end of the experiments. This work provides fundamental insights to understand the complex behavior of gaseous hydrocarbon mixtures in bioreactors, along with a systematic approach for the development of SIFT-MS methods.

Profiles, exposure assessment and expanded screening of PAHs and their derivatives in one petroleum refinery facility of China.

This study investigated environmental distribution and human exposure of polycyclic aromatic hydrocarbons (PAHs) and their derivatives in one Chinese petroleum refinery facility. It was found that, following with high concentrations of 16 EPA PAHs (∑Parent-PAHs) in smelting subarea of studied petroleum refinery facility, total derivatives of PAHs [named as XPAHs, including nitro PAHs (NPAHs), chlorinated PAHs (Cl-PAHs), and brominated PAHs (Br-PAHs)] in gas (mean= 1.57 × 104 ng/m3), total suspended particulate (TSP) (mean= 4.33 × 103 ng/m3) and soil (mean= 4.37 × 103 ng/g) in this subarea had 1.76-6.19 times higher levels than those from other subareas of this facility, surrounding residential areas and reference areas, indicating that petroleum refining processes would lead apparent derivation of PAHs. Especially, compared with those in residential and reference areas, gas samples in the petrochemical areas had higher ∑NPAH/∑PAHs (mean=2.18), but lower ∑Cl-PAH/∑PAHs (mean=1.43 × 10-1) and ∑Br-PAH/∑PAHs ratios (mean=7.49 × 10-2), indicating the richer nitrification of PAHs than chlorination during petrochemical process. The occupational exposure to PAHs and XPAHs in this petroleum refinery facility were 24-343 times higher than non-occupational exposure, and the ILCR (1.04 × 10-4) for petrochemical workers was considered to be potential high risk. Furthermore, one expanded high-resolution screening through GC Orbitrap/MS was performed for soils from petrochemical area, and another 35 PAHs were found, including alkyl-PAHs, phenyl-PAHs and other species, indicating that profiles and risks of PAHs analogs in petrochemical areas deserve further expanded investigation.

Accumulation analysis and overall measurement to represent airborne toxic metals with passive tree bark biomonitoring technique in urban areas.

Authorities have long proved the utility of bioindicators in monitoring the state of environmental pollution. Some biological indicators can measure environmental pollutant levels, and many tree species have been tested for suitability for monitoring purposes. The differences in morphological characteristics in the trees have demonstrated the effects of human activities on different materials. Measuring bark and wood biomass from contaminated sites was identified and directly compared with those from a clean site or areas characterized by distinct contamination sources. However, preliminary results demonstrate the approach's potential in the realization of strategies for disease control and promoting health to reduce environmental and health inequalities in at-risk urban areas. Picea orientalis L. and Cedrus atlantica Endl., especially their bark, can be regarded as a more robust storage of Cu (37.95 mg/kg) and Mn (188.25 mg/kg) than Pinus pinaster, Cupressus arizonica, and Pseudotsuga menziesii, which and is therefore a better bioindicator for Cu and Mn pollution. Considering the total concentrations as a result of the study, the pollution is thought to be caused by environmental problems and traffic in the region. The deposition of Cu, Mn, Ni, and Zn elements was found P. menziesii (60, 443, 58, and 258 mg/kg) and P. orientalis (76, 1684, 41, and 378 mg/kg) and seems to reflect atmospheric quite clearly compared to P. pinaster, C. arizonica, and C. atlantica. Ni and Zn concentrations have significantly increased since 1983, and P. menziesii and P. orientalis can be potentially valuable bioindicators for emphasizing polluted fields.

Bioavailable fractions of heavy metals in the road dust during infrastructure construction at urban Coimbatore and its potential health implications, India.

Over the past two decades the Global South is witnessing unprecedented economic transformation and Asian Cities in particular have a remarkable upsurge. Coimbatore, an industrial city in Southern India with an estimated population of 2 million (in 2022) is witnessing a rapid transition in terms of infrastructure development. In this context, the present study attempts to assess the particulate matter (PM10 and PM2.5) emissions at road network construction sites and the heavy metal fractionation in the road dust/sediment samples with a core focus to quantify the bioavailable fraction of metals (Fe, Cu, Cr, Cd, Pb and Ni) and its source apportionment in the road side dust/sediment samples. About 60 composite road dust/sediment samples were collected for heavy metal fractionation analysis in the six arterial roads that undergo core developments like construction of road over bridges, additional road incorporation and street expansions. PM monitoring revealed that 24 h average PM2.5 (47 µg/m3) and PM10 (69 µg/m3) concentrations at many construction sites exceeded 24 h average recommended by WHO guidelines [PM2.5 (15 µg/m3) and PM10 (45 µg/m3), respectively]. The bioavailable fractions of Fe, Cu, Cr and Cd are notably higher in the roadside sediment samples at road construction sites. Health Risk assessment, such as carcinogenic risks (Children-4.41 × 10-2, Adult-3.598 × 10-6) and non-carcinogenic risks, inferred substantial risks at high intensity construction sites with statistical analyses, including PCA and cluster analysis, indicating considerable anthropogenic influences in the heavy metal fractions.

Towards a low-carbon and beautiful world: assessing the impact of digital technology on the common benefits of pollution reduction and carbon reduction.

When ecology thrives, civilization thrives, and when ecology declines, civilization declines. Based on panel data from 30 provinces in China from 2000 to 2021, this study used marginal abatement costs to estimate the co-benefits of pollution reduction and carbon reduction. Two-way fixed effect and two-stage intermediary effect models were used to evaluate the impact of digital technology on co-benefits and its indirect channels. The results indicated that China's total carbon emissions maintained a steady growth trend, while air pollution showed a fluctuating declining trend. Reaching peak carbon neutrality calls for more innovative solutions. Under joint emission reduction efforts, the study revealed marginal abatement cost savings of 535.8 million yuan/million tons and 6216.5 million yuan/µg/m3 for carbon reduction and pollution reduction, respectively. Most importantly, the study confirmed that joint emission reduction programs can reduce environmental governance costs more than individual emission reductions can, and the co-benefits increased from 37.983 to 44.757. The co-benefits generally showed a trend of fluctuation and increases and had the characteristics of phased transformation. Intragroup differences and cross-overlapping between regions made regional differences in co-benefits obvious. The subversive, permeable, and integrated features of digital technology have resulted in the all-around transformation of the economy and society, and the new technology-economy paradigm has significantly improved co-benefits. The conclusion remains valid after robustness testing and controlling for endogeneity problems. The results of the mechanism analysis suggest that digital technology can indirectly improve synergies through the intermediary channels of fostering green technology innovation, reducing energy consumption intensity and improving the energy structure.

Spatio-temporal distribution and source contributions of the ambient pollutants in Lucknow city, India.

Clean air is imperative to the survival of all life forms on the planet. However, recent times have witnessed enormous escalation in urban pollution levels. It is therefore, incumbent upon us to decipher measures to deal with it. In perspective, the present study was carried out to assess PM10 and PM2.5 loading, metallic constituents, gaseous pollutants, source contributions, health impact and noise level of nine-locations, grouped as residential, commercial, and industrial in Lucknow city for 2019-21. Mean concentrations during pre-monsoon for PM10, PM2.5, SO2 and NO2 were: 138.2 ± 35.2, 69.1 ± 13.6, 8.5 ± 3.3 and 32.3 ± 7.4 µg/m3, respectively, whereas post-monsoon concentrations were 143.0 ± 33.3, 74.6 ± 14.5, 12.5 ± 2.1, and 35.5 ± 6.3 µg/m3, respectively. Exceedance percentage of pre-monsoon PM10 over National Ambient Air Quality Standards (NAAQS) was 38.2% while that for post-monsoon was 43.0%; whereas corresponding values for PM2.5 were 15.2% and 24.3%. Post-monsoon season showed higher particulate loading owing to wintertime inversion and high humidity conditions. Order of elements associated with PM2.5 is Co < Cd < Cr < Ni < V < Be < Mo < Mn < Ti < Cu < Pb < Se < Sr < Li < B < As < Ba < Mg < Al < Zn < Ca < Fe < K < Na and that with PM10 is Co < Cd < Ni < Cr < V < Ti < Be < Mo < Cu < Pb < Se < Sr < Li < B < As < Mn < Ba < Mg < Al < Fe < Zn < K < Na < Ca. WHO AIRQ + ascertained 1654, 144 and 1100 attributable cases per 0.1 million of population to PM10 exposure in 2019-21. Source apportionment was carried out using USEPA-PMF and resolved 6 sources with highest percent contributions including road dust re-entrainment, biomass burning and vehicular emission. It is observed that residents of Lucknow city regularly face exposure to particulate pollutants and associated constituents making it imperative to develop pollution abetment strategies.

Multi-source observations on the effect of atmospheric blocking on air quality in Istanbul: a study case.

Air pollution is affected by the atmospheric dynamics. This study aims to determine that air pollution concentration values in Istanbul increased significantly and reached peak values due to atmospheric blocking between the 30th of December 2022 and the 5th of January 2023. In this study, hourly pollutant data was obtained from 16 air quality monitoring stations (AQMS), the exact reanalysis data was extracted from ERA5 database, and inversion levels and meteorological and synoptic analyses were used to determine the effects of atmospheric blocking on air pollution. Also, cloud base heights and vertical visibility measurements were taken with a ceilometer. Statistical calculations and data visualizations were performed using the R and Grads program. Omega-type blocking, which started in Istanbul on December 30, 2022, had a significant impact on the 1st and 2nd of January 2023, and PM10 and PM2.5 concentration values reached their peak values at 572.8 and 254.20 µg/m3, respectively. In addition, it was found that the average concentration values in the examined period in almost all stations were higher than the averages for January and February. As a result, air quality in Istanbul was determined as "poor" between these calendar dates. It was found that the blocking did not affect the ozone (µg/m3) concentration. It was also found that the concentrations of particulate matter (PM) 10 µm or less in diameter (PM10) and PM 2.5 µm or less in diameter (PM2.5) were increased by the blocking effect in the Istanbul area. Finally, according to the data obtained using the ceilometer, cloud base heights decreased to 30 m and vertical visibility to 10 m.

Emission characteristics and health effects of PM2.5 from vehicles in typical areas.

Introduction: Vehicle emissions have become an important source of urban air pollution, and the assessment of air pollution emission characteristics and health effects caused by specific pollution sources can provide scientific basis for air quality management. Methods: In this paper, vehicle PM2.5 pollution in typical urban agglomerations of China (the Beijing-Tianjin-Hebei urban agglomeration (BTHUA), the triangle of the Central China urban agglomeration (TCCUA) and the Chengdu-Chongqing urban agglomeration (CCUA)) were used as research samples to evaluate the emission characteristics, health effects and economic losses of vehicle PM2.5 pollution based on the emission inventory, air quality model and exposure-response function from 2010 to 2020. Results: The results indicated that PM2.5 emissions from vehicles in the three urban agglomerations during 2010-2020 first showed an upward yearly trend and then showed a slow decrease in recent years. Heavy-duty trucks and buses are the main contribution vehicles of PM2.5, and the contribution rates of light-duty vehicles to PM2.5 is increasing year by year. The contribution rate of PM2.5 in Beijing decreased significantly. In addition to capital cities and municipalities directly under the central Government, the emission of pollutants in other cities cannot be ignored. The evaluation results of the impact of PM2.5 pollution from vehicles on population health show that: the number of each health endpoint caused by PM2.5 pollution from vehicles in the BTHUA and CCUA showed an overall upward trend, while the TCCUA showed a downward trend in recent years. Among them, PM2.5 pollution from vehicles in the three major urban agglomerations cause about 78,200 (95% CI: 20,500-138,800) premature deaths, 122,800 (95% CI: 25,600-220,500) inpatients, and 628,400 (95% CI: 307,400-930,400) outpatients and 1,332,400 (95% CI: 482,700-2,075,600) illness in 2020. The total health economic losses caused by PM2.5 pollution from vehicles in the three major urban agglomerations in 2010, 2015 and 2020 were 68.25 billion yuan (95% CI: 21.65-109.16), 206.33 billion yuan (95% CI: 66.20-326.20) and 300.73 billion yuan (95% CI: 96.79-473.16), accounting for 0.67% (95% CI: 0.21-1.07%), 1.19% (95% CI: 0.38%-1.88%) and 1.21% (95% CI: 0.39%-1.90%) of the total GDP of these cities. Discussion: Due to the differences in vehicle population, PM2.5 concentration, population number and economic value of health terminal units, there are differences in health effects and economic losses among different cities in different regions. Among them, the problems of health risks and economic losses were relatively prominent in Beijing, Chengdu, Chongqing, Tianjin and Wuhan.

Global health costs of ambient PM2·5 from combustion sources: a modelling study supporting air pollution control strategies.

BACKGROUND: Climate actions targeting combustion sources can generate large ancillary health benefits via associated air-quality improvements. Therefore, understanding the health costs associated with ambient fine particulate matter (PM2·5) from combustion sources can guide policy design for both air pollution and climate mitigation efforts. METHODS: In this modelling study, we estimated the health costs attributable to ambient PM2·5 from six major combustion sources across 204 countries using updated concentration-response models and an age-adjusted valuation method. We defined major combustion sources as the sum of total coal, liquid fuel and natural gas, solid biofuel, agricultural waste burning, other fires, and 50% of the anthropogenic fugitive, combustion, and industrial dust source. FINDINGS: Global long-term exposure to ambient PM2·5 from combustion sources imposed US$1·1 (95% uncertainty interval 0·8-1·5) trillion in health costs in 2019, accounting for 56% of the total health costs from all PM2·5 sources. Comparing source contributions to PM2·5 concentrations and health costs, we observed a higher share of health costs from combustion sources compared to their contribution to population-weighted PM2·5 concentration across 134 countries, accounting for more than 87% of the global population. This disparity was primarily attributed to the non-linear relationship between PM2·5 concentration and its associated health costs. Globally, phasing out fossil fuels can generate 23% higher relative health benefits compared to their share of PM2·5 reductions. Specifically, the share of health costs for total coal was 36% higher than the source's contributions to corresponding PM2·5 concentrations and the share of health costs for liquid fuel and natural gas was 12% higher. Other than fossil fuels, South Asia was expected to show 16% greater relative health benefits than the percentage reduction in PM2·5 from the abatement of solid biofuel emissions. INTERPRETATION: In most countries, targeting combustion sources might offer greater health benefits than non-combustion sources. This finding provides additional rationale for climate actions aimed at phasing out combustion sources, especially those related to fossil fuels and solid biofuel. Mitigation efforts designed according to source-specific health costs can more effectively avoid health costs than strategies that depend solely on the source contributions to overall PM2·5 concentration. FUNDING: The Health Effects Institute, the National Natural Science Foundation of China, and NASA.

Ambient air pollution and daily mortality in ten cities of India: a causal modelling study.

BACKGROUND: The evidence for acute effects of air pollution on mortality in India is scarce, despite the extreme concentrations of air pollution observed. This is the first multi-city study in India that examines the association between short-term exposure to PM2·5 and daily mortality using causal methods that highlight the importance of locally generated air pollution. METHODS: We applied a time-series analysis to ten cities in India between 2008 and 2019. We assessed city-wide daily PM2·5 concentrations using a novel hybrid nationwide spatiotemporal model and estimated city-specific effects of PM2·5 using a generalised additive Poisson regression model. City-specific results were then meta-analysed. We applied an instrumental variable causal approach (including planetary boundary layer height, wind speed, and atmospheric pressure) to evaluate the causal effect of locally generated air pollution on mortality. We obtained an integrated exposure-response curve through a multivariate meta-regression of the city-specific exposure-response curve and calculated the fraction of deaths attributable to air pollution concentrations exceeding the current WHO 24 h ambient PM2·5 guideline of 15 µg/m3. To explore the shape of the exposure-response curve at lower exposures, we further limited the analyses to days with concentrations lower than the current Indian standard (60 µg/m3). FINDINGS: We observed that a 10 µg/m3 increase in 2-day moving average of PM2·5 was associated with 1·4% (95% CI 0·7-2·2) higher daily mortality. In our causal instrumental variable analyses representing the effect of locally generated air pollution, we observed a stronger association with daily mortality (3·6% [2·1-5·0]) than our overall estimate. Our integrated exposure-response curve suggested steeper slopes at lower levels of exposure and an attenuation of the slope at high exposure levels. We observed two times higher risk of death per 10 µg/m3 increase when restricting our analyses to observations below the Indian air quality standard (2·7% [1·7-3·6]). Using the integrated exposure-response curve, we observed that 7·2% (4·2%-10·1%) of all daily deaths were attributed to PM2·5 concentrations higher than the WHO guidelines. INTERPRETATION: Short-term PM2·5 exposure was associated with a high risk of death in India, even at concentrations well below the current Indian PM2·5 standard. These associations were stronger for locally generated air pollutants quantified through causal modelling methods than conventional time-series analysis, further supporting a plausible causal link. FUNDING: Swedish Research Council for Sustainable Development.

All-cause, cardiovascular, and respiratory mortality and wildfire-related ozone: a multicountry two-stage time series analysis.

BACKGROUND: Wildfire activity is an important source of tropospheric ozone (O3) pollution. However, no study to date has systematically examined the associations of wildfire-related O3 exposure with mortality globally. METHODS: We did a multicountry two-stage time series analysis. From the Multi-City Multi-Country (MCC) Collaborative Research Network, data on daily all-cause, cardiovascular, and respiratory deaths were obtained from 749 locations in 43 countries or areas, representing overlapping periods from Jan 1, 2000, to Dec 31, 2016. We estimated the daily concentration of wildfire-related O3 in study locations using a chemical transport model, and then calibrated and downscaled O3 estimates to a resolution of 0·25°â€ˆ× 0·25° (approximately 28 km2 at the equator). Using a random-effects meta-analysis, we examined the associations of short-term wildfire-related O3 exposure (lag period of 0-2 days) with daily mortality, first at the location level and then pooled at the country, regional, and global levels. Annual excess mortality fraction in each location attributable to wildfire-related O3 was calculated with pooled effect estimates and used to obtain excess mortality fractions at country, regional, and global levels. FINDINGS: Between 2000 and 2016, the highest maximum daily wildfire-related O3 concentrations (≥30 µg/m3) were observed in locations in South America, central America, and southeastern Asia, and the country of South Africa. Across all locations, an increase of 1 µg/m3 in the mean daily concentration of wildfire-related O3 during lag 0-2 days was associated with increases of 0·55% (95% CI 0·29 to 0·80) in daily all-cause mortality, 0·44% (-0·10 to 0·99) in daily cardiovascular mortality, and 0·82% (0·18 to 1·47) in daily respiratory mortality. The associations of daily mortality rates with wildfire-related O3 exposure showed substantial geographical heterogeneity at the country and regional levels. Across all locations, estimated annual excess mortality fractions of 0·58% (95% CI 0·31 to 0·85; 31 606 deaths [95% CI 17 038 to 46 027]) for all-cause mortality, 0·41% (-0·10 to 0·91; 5249 [-1244 to 11 620]) for cardiovascular mortality, and 0·86% (0·18 to 1·51; 4657 [999 to 8206]) for respiratory mortality were attributable to short-term exposure to wildfire-related O3. INTERPRETATION: In this study, we observed an increase in all-cause and respiratory mortality associated with short-term wildfire-related O3 exposure. Effective risk and smoke management strategies should be implemented to protect the public from the impacts of wildfires. FUNDING: Australian Research Council and the Australian National Health and Medical Research Council.

The impact of urban configuration types on urban heat islands, air pollution, CO2 emissions, and mortality in Europe: a data science approach.

BACKGROUND: The world is becoming increasingly urbanised. As cities around the world continue to grow, it is important for urban planners and policy makers to understand how different urban configuration patterns affect the environment and human health. However, previous studies have provided mixed findings. We aimed to identify European urban configuration types, on the basis of the local climate zones categories and street design variables from Open Street Map, and evaluate their association with motorised traffic flows, surface urban heat island (SUHI) intensities, tropospheric NO2, CO2 per person emissions, and age-standardised mortality. METHODS: We considered 946 European cities from 31 countries for the analysis defined in the 2018 Urban Audit database, of which 919 European cities were analysed. Data were collected at a 250 m × 250 m grid cell resolution. We divided all cities into five concentric rings based on the Burgess concentric urban planning model and calculated the mean values of all variables for each ring. First, to identify distinct urban configuration types, we applied the Uniform Manifold Approximation and Projection for Dimension Reduction method, followed by the k-means clustering algorithm. Next, statistical differences in exposures (including SUHI) and mortality between the resulting urban configuration types were evaluated using a Kruskal-Wallis test followed by a post-hoc Dunn's test. FINDINGS: We identified four distinct urban configuration types characterising European cities: compact high density (n=246), open low-rise medium density (n=245), open low-rise low density (n=261), and green low density (n=167). Compact high density cities were a small size, had high population densities, and a low availability of natural areas. In contrast, green low density cities were a large size, had low population densities, and a high availability of natural areas and cycleways. The open low-rise medium and low density cities were a small to medium size with medium to low population densities and low to moderate availability of green areas. Motorised traffic flows and NO2 exposure were significantly higher in compact high density and open low-rise medium density cities when compared with green low density and open low-rise low density cities. Additionally, green low density cities had a significantly lower SUHI effect compared with all other urban configuration types. Per person CO2 emissions were significantly lower in compact high density cities compared with green low density cities. Lastly, green low density cities had significantly lower mortality rates when compared with all other urban configuration types. INTERPRETATION: Our findings indicate that, although the compact city model is more sustainable, European compact cities still face challenges related to poor environmental quality and health. Our results have notable implications for urban and transport planning policies in Europe and contribute to the ongoing discussion on which city models can bring the greatest benefits for the environment, climate, and health. FUNDING: Spanish Ministry of Science and Innovation, State Research Agency, Generalitat de Catalunya, Centro de Investigación Biomédica en red Epidemiología y Salud Pública, and Urban Burden of Disease Estimation for Policy Making as a Horizon Europe project.

Air Quality and Public Health Risk Assessment: A Case of an Industrial Area in Haridwar City, Uttarakhand (India).

BACKGROUND: Air pollution is a significant issue for a developing country like India and the air quality index (AQI) forecasting helps to predict air quality levels in advance and allows individuals to take precautionary measures to protect their health. OBJECTIVES: The study aimed to forecast the AQI for an industrial area (SIDCUL, Haridwar City) using a time series regression model. MATERIALS AND METHODS: Three years of existing AQI data points (post-COVID-19) were collected from the Uttarakhand Pollution Control Board for the SIDCUL area of Haridwar City and tried to know the status of AQI values for the following 12 months. Trend and seasonality components were seen through the decomposition process. Further, the augmented Dickey-Fuller test was applied to check the stationarity of the series before finalizing the best-suited time series model for forecasting the AQI values. RESULTS: With the help of autocorrelation function (ACF)/partial ACF plots, a seasonal autoregressive integrated moving average (ARIMA) (0,1,0) (1,0,0)[12] model was selected with the minimum akaike information criterion (253.143) and mean absolute percentage error (17.42%). The AQI values have also been forecasted for this industrial area (SIDCUL) for the following year. CONCLUSION: The seasonal ARIMA (0,1,0) (1,0,0)[12] model may be helpful to forecast the AQI values for a nonstationary time series dataset. Research indicates that the air of the SIDCUL area will become moderately polluted and may cause breathing discomfort to asthma patients' health. The scientists might apply this model to other polluted regions of the country so that the public and the government can take preventive measures in advance.

Association of volatile organic compound levels with chronic obstructive pulmonary diseases in NHANES 2013-2016.

Volatile organic compounds (VOCs) represent a significant component of air pollution. However, studies evaluating the impact of VOC exposure on chronic obstructive pulmonary disease (COPD) have predominantly focused on single pollutant models. This study aims to comprehensively assess the relationship between multiple VOC exposures and COPD. A large cross-sectional study was conducted on 4983 participants from the National Health and Nutrition Examination Survey. Four models, including weighted logistic regression, restricted cubic splines (RCS), weighted quantile sum regression (WQS), and the dual-pollution model, were used to explore the association between blood VOC levels and the prevalence of COPD in the U.S. general population. Additionally, six machine learning algorithms were employed to develop a predictive model for COPD risk, with the model's predictive capacity assessed using the area under the curve (AUC) indices. Elevated blood concentrations of benzene, toluene, ortho-xylene, and para-xylene were significantly associated with the incidence of COPD. RCS analysis further revealed a non-linear and non-monotonic relationship between blood levels of toluene and m-p-xylene with COPD prevalence. WQS regression indicated that different VOCs had varying effects on COPD, with benzene and ortho-xylene having the greatest weights. Among the six models, the Extreme Gradient Boosting (XGBoost) model demonstrated the strongest predictive power, with an AUC value of 0.781. Increased blood concentrations of benzene and toluene are significantly correlated with a higher prevalence of COPD in the U.S. population, demonstrating a non-linear relationship. Exposure to environmental VOCs may represent a new risk factor in the etiology of COPD.

Using Parametric g-Computation for Time-to-Event Data and Distributed Lag Models to Identify Critical Exposure Windows for Preterm Birth: An Illustrative Example Using PM2.5 in a Retrospective Birth Cohort Based in Eastern Massachusetts (2011-2016).

BACKGROUND: Parametric g-computation is an attractive analytic framework to study the health effects of air pollution. Yet, the ability to explore biologically relevant exposure windows within this framework is underdeveloped. OBJECTIVES: We outline a novel framework for how to incorporate complex lag-responses using distributed lag models (DLMs) into parametric g-computation analyses for survival data. We call this approach "g-survival-DLM" and illustrate its use examining the association between PM2.5 during pregnancy and the risk of preterm birth (PTB). METHODS: We applied the g-survival-DLM approach to estimate the hypothetical static intervention of reducing average PM2.5 in each gestational week by 20% on the risk of PTB among 9,403 deliveries from Beth Israel Deaconess Medical Center, Boston, Massachusetts, 2011-2016. Daily PM2.5 was taken from a 1-km grid model and assigned to address at birth. Models were adjusted for sociodemographics, time trends, nitrogen dioxide, and temperature. To facilitate implementation, we provide a detailed description of the procedure and accompanying R syntax. RESULTS: There were 762 (8.1%) PTBs in this cohort. The gestational week-specific median PM2.5 concentration was relatively stable across pregnancy at ∼7µg/m3. We found that our hypothetical intervention strategy changed the cumulative risk of PTB at week 36 (i.e., the end of the preterm period) by -0.009 (95% confidence interval: -0.034, 0.007) in comparison with the scenario had we not intervened, which translates to about 86 fewer PTBs in this cohort. We also observed that the critical exposure window appeared to be weeks 5-20. DISCUSSION: We demonstrate that our g-survival-DLM approach produces easier-to-interpret, policy-relevant estimates (due to the g-computation); prevents immortal time bias (due to treating PTB as a time-to-event outcome); and allows for the exploration of critical exposure windows (due to the DLMs). In our illustrative example, we found that reducing fine particulate matter [particulate matter (PM) with aerodynamic diameter ≤2.5µm (PM2.5)] during gestational weeks 5-20 could potentially lower the risk of PTB. https://doi.org/10.1289/EHP13891.

Investigating the effect of dynamic traffic distribution on network-wide traffic emissions: An empirical study in Ningbo, China.

Urban road traffic is one of the primary sources of carbon emissions. Previous studies have demonstrated the close relationship between traffic flow characteristics and carbon emissions (CO2). However, the impact of dynamic traffic distribution on carbon emissions is rarely empirically studied on the network level. To fill this gap, this study proposes a dynamic network carbon emissions estimation method. The network-level traffic emissions are estimated by combining macroscopic emission models and recent advances in dynamic network traffic flow modeling, namely, Macroscopic Fundamental Diagram. The impact of traffic distribution and the penetration of battery electric vehicles on total network emissions are further investigated using the Monte Carlo method. The results indicate the substantial effect of network traffic distribution on carbon emissions. Using the urban expressway network in Ningbo as an example, in the scenario of 100% internal combustion engine vehicles, increasing the standard deviation of link-level traffic density from 0 to 15 veh/km-ln can result in an 8.9% network capacity drop and a 15.5% reduction in network carbon emissions. This effect can be moderated as the penetration rate of battery electric vehicles increases. Based on the empirical and simulating evidence, different expressway pollution management strategies can be implemented, such as petrol vehicle restrictions, ramp metering, congestion pricing, and perimeter control strategies.

Study on carbon emission driving factors and carbon peak forecasting in power sector of Shanxi province.

The realisation of the low-carbon transition of the energy system in resource-intensive regions, as embodied by Shanxi Province, depends on a thorough understanding of the factors impacting the power sector's carbon emissions and an accurate prediction of the peak trend. Because of this, the power industry's carbon emissions in Shanxi province are measured in this article from 1995 to 2020 using data from the Intergovernmental Panel on Climate Change (IPCC). To obtain a deeper understanding of the factors impacting carbon emissions in the power sector, factor decomposition is performed using the Logarithmic Mean Divisia Index (LMDI). Second, in order to precisely mine the relationship between variables and carbon emissions, the Sparrow Search Algorithm (SSA) aids in the optimisation of the Long Short-Term Memory (LSTM). In order to implement SSA-LSTM-based carbon peak prediction in the power industry, four development scenarios are finally built up. The findings indicate that: (1) There has been a fluctuating upward trend in Shanxi Province's total carbon emissions from the power industry between 1995 and 2020, with a cumulative growth of 372.10 percent. (2) The intensity of power consumption is the main factor restricting the rise of carbon emissions, contributing -65.19%, while the per capita secondary industry contribution factor, contributing 158.79%, is the main driver of the growth in emissions. (3) While the baseline scenario and the rapid development scenario fail to peak by 2030, the low carbon scenario and the green development scenario peak at 243,991,100 tonnes and 258,828,800 tonnes, respectively, in 2025 and 2028. (4) Based on the peak performance and the decomposition results, resource-intensive cities like Shanxi's power industry should concentrate on upgrading and strengthening the industrial structure, getting rid of obsolete production capacity, and encouraging the faster development of each factor in order to help the power sector reach peak carbon performance.

Investigating the impact of urban-environmental factors on air pollutants: a land use regression model approach and health risk assessment.

The presence of pollutants in the earth's atmosphere has a direct impact on human health and the environment. So that pollutants such as carbon monoxide (CO) and particulate matter (PM) cause respiratory diseases, cough headache, etc. Since the amount of pollutants in the air is related to environmental and urban factors, the aim of the current research is to investigate the relationship between the concentration of CO, PM2.5 and PM10 with urban-environmental factors including land use, wind speed and wind direction, topography, traffic, road network, and population through a Land use regression (LUR) model. The concentrations of CO, PM2.5 and PM10 were measured during four seasons from 26th of March 2022 to 16th of March 2023 at 25 monitoring stations and then the information about pollutant measurement points and Land use data were entered into the ArcGIS software. The annual average concentrations of CO, PM2.5 and PM10 were 0.7 ppm, 18.94 and 60.76 µg/m3, respectively, in which the values of annual average concentration of CO and PMs were outside the air quality guideline standard. The results of the health risk assessment showed that the hazard quotient values for all three investigated pollutants were lower than 1 and therefore, they were not in adverse conditions in terms of health effects. Among the urban-environmental factors affecting air pollution, the traffic variable is the most important factor affecting the annual LUR model of CO, PM2.5 and PM10, and then the topography variable is the second most effective factor on the annual LUR model of the aforementioned pollutants.

Health and environmental risk assessment of mercury in outdoor and indoor dust in artisanal and small-scale gold mining area in Amansie-west district in the Ashanti Region of Ghana.

Mercury (Hg) pollution around artisanal and small-scale gold mining (ASGM) areas has been of much concern. Many studies have reported elevated Hg concentrations in environmental media, but studies on dust relating to inhalation exposure of Hg around ASGM area are limited. In this study, we investigated Hg in indoor and outdoor dust to reveal environmental and human health risk around ASGM in Amansie West district, Ghana. Indoor and outdoor dust samples were collected from Manso Abore and Manso Nkwanta in Ashanti Region. Concentration of Hg in the samples were analyzed using a direct Hg analyzer. The mean and median value of Hg concentration in the indoor dust (n = 31) were 2.2 ± 3.6 mg/kg and 0.72 mg/kg respectively while that of the outdoor dust (n = 60) were 0.19 ± 0.48 mg/kg and 0.042 mg/kg, respectively. The mean and median Hg concentration in indoor dust were about 11 and 17 times higher respectively than that in the outdoor dust. The Hg concentration in the indoor dust was statistically significantly higher than that of the non-miner in Manso Abore (p < 0.05) but was not significant in Manso Nkwanta, probably due to higher mining activity. The geo-accumulation index of the outdoor dust ranged from unpolluted to extremely polluted while that of the indoor dust ranged from moderately polluted to extremely polluted. Health risk assessments suggested that there was no potential non-carcinogenic health effect for Hg exposure relating to the dust to residents living in rooms of miners and non-miners.