Accumulation of Atmospheric PAHs in White Mustard - Can the Seeds Be Affected?

Traffic-related particulate matter emissions have been considerably reduced due to stringent regulations in Europe. However, emission of diesel-powered vehicles still poses a significant environmental threat, affecting rural ecosystems and agriculture. Several studies have reported that polycyclic aromatic hydrocarbons (PAHs), a group of potentially toxic organic compounds, can accumulate in crops and vegetables. In our study, white mustard (Sinapis alba L.) plants were experimentally treated with an extract of diesel exhaust. PAH concentrations were measured in the different plant compartments (stems, leaves and seeds), bioconcentration factors (BCFs) were also calculated. Significant accumulation was measured in the leaves and seeds, stems showed lower accumulation potential. All plant matrices showed high tendency to accumulate higher molecular weight PAHs, BCF was the highest in the 6-ring group. The fact that considerable accumulation was experienced in the seeds might show the risk of cultivating crops nearby roads highly impacted by traffic-related emissions.

Long-term exposure to PM2.5 and mortality in a national cohort in South Korea: effect modification by community deprivation, medical infrastructure, and greenness.

BACKGROUND: Long-term exposure to PM2.5 has been linked to increased mortality risk. However, limited studies have examined the potential modifying effect of community-level characteristics on this association, particularly in Asian contexts. This study aimed to estimate the effects of long-term exposure to PM2.5 on mortality in South Korea and to examine whether community-level deprivation, medical infrastructure, and greenness modify these associations. METHODS: We conducted a nationwide cohort study using the National Health Insurance Service-National Sample Cohort. A total of 394,701 participants aged 30 years or older in 2006 were followed until 2019. Based on modelled PM2.5 concentrations, 1 to 3-year and 5-year moving averages of PM2.5 concentrations were assigned to each participant at the district level. Time-varying Cox proportional-hazards models were used to estimate the association between PM2.5 and non-accidental, circulatory, and respiratory mortality. We further conducted stratified analysis by community-level deprivation index, medical index, and normalized difference vegetation index to represent greenness. RESULTS: PM2.5 exposure, based on 5-year moving averages, was positively associated with non-accidental (Hazard ratio, HR: 1.10, 95% Confidence Interval, CI: 1.01, 1.20, per 10 µg/m3 increase) and circulatory mortality (HR: 1.22, 95% CI: 1.01, 1.47). The 1-year moving average of PM2.5 was associated with respiratory mortality (HR: 1.33, 95% CI: 1.05, 1.67). We observed higher associations between PM2.5 and mortality in communities with higher deprivation and limited medical infrastructure. Communities with higher greenness showed lower risk for circulatory mortality but higher risk for respiratory mortality in association with PM2.5. CONCLUSIONS: Our study found mortality effects of long-term PM2.5 exposure and underlined the role of community-level factors in modifying these association. These findings highlight the importance of considering socio-environmental contexts in the design of air quality policies to reduce health disparities and enhance overall public health outcomes.

Short-term prediction of PM2.5 concentration by hybrid neural network based on sequence decomposition.

Accurate forecasting of PM2.5 concentrations serves as a critical tool for mitigating air pollution. This study introduces a novel hybrid prediction model, termed MIC-CEEMDAN-CNN-BiGRU, for short-term forecasting of PM2.5 concentrations using a 24-hour historical data window. Utilizing the Maximal Information Coefficient (MIC) for feature selection, the model integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Network (CNN), and Bidirectional Recurrent Gated Neural Network (BiGRU) to optimize predictive accuracy. We used 2016 PM2.5 monitoring data from Beijing, China as the empirical basis of this study and compared the model with several deep learning frameworks. RNN, LSTM, GRU, and other hybrid models based on GRU, respectively. The experimental results show that the prediction results of the hybrid model proposed in this question are more accurate than those of other models, and the R2 of the hybrid model proposed in this paper improves the R2 by nearly 5 percentage points compared with that of the single model; reduces the MAE by nearly 5 percentage points; and reduces the RMSE by nearly 11 percentage points. The results show that the hybrid prediction model proposed in this study is more accurate than other models in predicting PM2.5.

Distribution of 35 Polycyclic Aromatic Hydrocarbons in Pine Needle Samples from Selected Locations in the Republic of Korea.

Polycyclic aromatic hydrocarbons (PAHs), dust, and wax were measured in pine needles, and PAHs were also measured in surface soil. Pearson correlation analysis was performed between the analytical values. The main compounds responsible for the increase in total PAHs were non-carcinogenic phenanthrene and fluoranthene. Therefore, the % content of carcinogenic PAHs decreased with a slope = -0.037 (r = 0.47, p < 0.01), as the total PAH concentration in pine needles increased. Correlations between individual PAHs in pine needles and surface soil were very high when only low-number ring PAHs (2R- and 3R-PAHs) were statistically analyzed and significant when only high-number ring PAHs were statistically analyzed. Low-number ring PAH mainly moves in the gas phase and diffuses into the wax layer, so it was found to be statistically significant with the wax content of pine needles. High-number ring PAHs showed a high correlation with the amount of dust in pine needles because they mainly attached to dust particles and accumulated on the surface of pine needles. The ratios of fluoranthene/pyrene and methylphenanthrene/phenanthrene for predicting the origin of atmospheric PAHs have also been proven valid for pine needles.

Assessing Tunisia's urban air quality using combined lichens and Sentinel-5 satellite integration.

In Tunisia, urban air pollution is becoming a bigger problem. This study used a combined strategy of biomonitoring with lichens and satellite mapping with Sentinel-5 satellite data processed in Google Earth Engine (GEE) to assess the air quality over metropolitan Tunis. Lichen diversity was surveyed across the green spaces of the Faculty of Science of Tunisia sites, revealing 15 species with a predominance of pollution-tolerant genera. The Index of Atmospheric Purity (IAP) calculated from the lichen data indicated poor air quality. Spatial patterns of pollutants sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), and aerosol index across Greater Tunis were analyzed from Sentinel-5 datasets on the GEE platform. The higher values of these indices in the research area indicate that it may be impacted by industrial activity and highlight the considerable role that vehicle traffic plays in air pollution. The results of the IAP, IBL, and the combined ground-based biomonitoring and satellite mapping techniques confirm poor air quality and an environment affected by atmospheric pollutants which will enable proactive air quality management strategies to be put in place in Tunisia's rapidly expanding cities.

Global nitrous oxide emissions from livestock manure during 1890-2020: An IPCC tier 2 inventory.

Nitrous oxide (N2O) emissions from livestock manure contribute significantly to the growth of atmospheric N2O, a powerful greenhouse gas and dominant ozone-depleting substance. Here, we estimate global N2O emissions from livestock manure during 1890-2020 using the tier 2 approach of the 2019 Refinement to the 2006 IPCC Guidelines. Global N2O emissions from livestock manure increased by ~350% from 451 [368-556] Gg N year-1 in 1890 to 2042 [1677-2514] Gg N year-1 in 2020. These emissions contributed ~30% to the global anthropogenic N2O emissions in the decade 2010-2019. Cattle contributed the most (60%) to the increase, followed by poultry (19%), pigs (15%), and sheep and goats (6%). Regionally, South Asia, Africa, and Latin America dominated the growth in global emissions since the 1990s. Nationally, the largest emissions were found in India (329 Gg N year-1), followed by China (267 Gg N year-1), the United States (163 Gg N year-1), Brazil (129 Gg N year-1) and Pakistan (102 Gg N year-1) in the 2010s. We found a substantial impact of livestock productivity, specifically animal body weight and milk yield, on the emission trends. Furthermore, a large spread existed among different methodologies in estimates of global N2O emission from livestock manure, with our results 20%-25% lower than those based on the 2006 IPCC Guidelines. This study highlights the need for robust time-variant model parameterization and continuous improvement of emissions factors to enhance the precision of emission inventories. Additionally, urgent mitigation is required, as all available inventories indicate a rapid increase in global N2O emissions from livestock manure in recent decades.

The Long Hazy Tail: Analysis of the Impacts and Trends of Severe Outdoor and Indoor Air Pollution in North China.

China, especially the densely populated North China region, experienced severe haze events in the past decade that concerned the public. Although the most extreme cases have been largely eliminated through recent mitigation measures, severe outdoor air pollution persists and its environmental impact needs to be understood. Severe indoor pollution draws less public attention due to the short visible distance indoors, but its public health impacts cannot be ignored. Herein, we assess the trends and impacts of severe outdoor and indoor air pollution in North China from 2014 to 2021. Our results demonstrate the uneven contribution of severe hazy days to ambient and exposure concentrations of particulate matter with an aerodynamic diameter <2.5 (PM2.5). Although severe indoor pollution contributes to indoor PM2.5 concentrations (23%) to a similar extent as severe haze contributes to ambient PM2.5 concentrations (21%), the former's contribution to premature deaths was significantly higher. Furthermore, residential emissions contributed more in the higher PM2.5 concentration range both indoors and outdoors. Notably, severe haze had greater health impacts on urban residents, while severe indoor pollution was more impactful in rural areas. Our findings suggest that, besides reducing severe haze, mitigating severe indoor pollution is an important aspect of combating air pollution, especially toward improving public health.

Understanding the Disparities of PM2.5 Air Pollution in Urban Areas via Deep Support Vector Regression.

In densely populated urban areas, PM2.5 has a direct impact on the health and quality of residents' life. Thus, understanding the disparities of PM2.5 is crucial for ensuring urban sustainability and public health. Traditional prediction models often overlook the spillover effects within urban areas and the complexity of the data, leading to inaccurate spatial predictions of PM2.5. We propose Deep Support Vector Regression (DSVR) that models the urban areas as a graph, with grid center points as the nodes and the connections between grids as the edges. Nature and human activity features of each grid are initialized as the representation of each node. Based on the graph, DSVR uses random diffusion-based deep learning to quantify the spillover effects of PM2.5. It leverages random walk to uncover more extensive spillover relationships between nodes, thereby capturing both the local and nonlocal spillover effects of PM2.5. And then it engages in predictive learning using the feature vectors that encapsulate spillover effects, enhancing the understanding of PM2.5 disparities and connections across different regions. By applying our proposed model in the northern region of New York for predictive performance analysis, we found that DSVR consistently outperforms other models. During periods of PM2.5 surges, the R-square of DSVR reaches as high as 0.729, outperforming non-spillover models by 2.5 to 5.7 times and traditional spatial metric models by 2.2 to 4.6 times. Therefore, our proposed model holds significant importance for understanding disparities of PM2.5 air pollution in urban areas, taking the first steps toward a new method that considers both the spillover effects and nonlinear feature of data for prediction.

The effects of meteorological factors and air pollutants on the incidence of tuberculosis in people living with HIV/AIDS in subtropical Guangxi, China.

BACKGROUND: Previous studies have shown the association between tuberculosis (TB) and meteorological factors/air pollutants. However, little information is available for people living with HIV/AIDS (PLWHA), who are highly susceptible to TB. METHOD: Data regarding TB cases in PLWHA from 2014 to2020 were collected from the HIV antiviral therapy cohort in Guangxi, China. Meteorological and air pollutants data for the same period were obtained from the China Meteorological Science Data Sharing Service Network and Department of Ecology and Environment of Guangxi. A distribution lag non-linear model (DLNM) was used to evaluate the effects of meteorological factors and air pollutant exposure on the risk of TB in PLWHA. RESULTS: A total of 2087 new or re-active TB cases were collected, which had a significant seasonal and periodic distribution. Compared with the median values, the maximum cumulative relative risk (RR) for TB in PLWHA was 0.663 (95% confidence interval [CI]: 0.507-0.866, lag 4 weeks) for a 5-unit increase in temperature, and 1.478 (95% CI: 1.116-1.957, lag 4 weeks) for a 2-unit increase in precipitation. However, neither wind speed nor PM10 had a significant cumulative lag effect. Extreme analysis demonstrated that the hot effect (RR = 0.638, 95%CI: 0.425-0.958, lag 4 weeks), the rainy effect (RR = 0.285, 95%CI: 0.135-0.599, lag 4 weeks), and the rainless effect (RR = 0.552, 95%CI: 0.322-0.947, lag 4 weeks) reduced the risk of TB. Furthermore, in the CD4(+) T cells < 200 cells/µL subgroup, temperature, precipitation, and PM10 had a significant hysteretic effect on TB incidence, while temperature and precipitation had a significant cumulative lag effect. However, these effects were not observed in the CD4(+) T cells ≥ 200 cells/µL subgroup. CONCLUSION: For PLWHA in subtropical Guangxi, temperature and precipitation had a significant cumulative effect on TB incidence among PLWHA, while air pollutants had little effect. Moreover, the influence of meteorological factors on the incidence of TB also depends on the immune status of PLWHA.

Assessing the relationship between the biochemical and the morphological factors (leaf surface area and leaf surface texture) of industrial and roadside plants.

The study of biochemical parameters provides an idea of the resistance of plants against air pollutants. Biochemical and Physiological parameters are studied with the help of Air pollution tolerance index (APTI). Fifteen plant species were evaluated to assess biochemical and APTI from two polluted sites (Phagwara Industrial area and Phagwara Bus stand area). The values of APTI were found to be highest for Mangifera indica (19.6), Ficus religiosa (19.3), and Ficus benghalensis (15.8) in the industrial area. On the roadside, Mangifera indica (16.8), Ficus benghalensis (16.5), and Ficus religiosa (16.4). Mangifera indica, Ficus religiosa, and Ficus benghalensis were found to be excellent performers in reducing pollution at both the sampling sites as per the APTI values. The order of tolerance was Mangifera indica > Ficus religiosa > Ficus benghalensis > Polyalthia longifolia > Mentha piperita in both the polluted sites. Morphological changes were observed in the plants, suggesting the possibility of pollution stress, which is probably responsible for the changes in biochemical parameters. As a result, the relationship between morphological and biochemical parameters of selected plant species growing in roadside and industrial areas was explored. The findings revealed that relative water content showed a significant positive and negative correlation with leaf surface texture and leaf surface area. On the other hand, ascorbic acid showed a significant positive correlation with them. In conclusion, it has been studied that morphological parameters including biochemical parameters can be proved to be important in investigating the ability of plants to cope with air pollution and in calculating tolerance index.

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.

Placental DNA methylation signatures of prenatal air pollution exposure and potential effects on birth outcomes: an analysis of three prospective cohorts.

BACKGROUND: Pregnancy air pollution exposure (PAPE) has been linked to a wide range of adverse birth and childhood outcomes, but there is a paucity of data on its influence on the placental epigenome, which can regulate the programming of physiological functions and affect child development. This study aimed to investigate the association between prenatal air pollutant exposure concentrations and changes in placental DNA methylation patterns, and to explore the potential windows of susceptibility and sex-specific alterations. METHODS: This multi-site study used three prospective population-based mother-child cohorts: EDEN, PELAGIE, and SEPAGES, originating from four French geographical regions (Nancy, Poitiers, Brittany, and Grenoble). Pregnant women were included between 2003 and 2006 for EDEN and PELAGIE, and between 2014 and 2017 for SEPAGES. The main eligibility criteria were: being older than 18 years, having a singleton pregnancy, and living and planning to deliver in one of the maternity clinics in one of the study areas. A total of 1539 mother-child pairs were analysed, measuring placental DNA methylation using Illumina BeadChips. We used validated spatiotemporally resolved models to estimate PM2·5, PM10, and NO2 exposure over each trimester of pregnancy at the maternal residential address. We conducted a pooled adjusted epigenome-wide association study to identify differentially methylated 5'-C-phosphate-G-3' (CpG) sites and regions (assessed using the Infinium HumanMethylationEPIC BeadChip array, n=871), including sex-specific and sex-linked alterations, and independently validated our results (assessed using the Infinium HumanMethylation450 BeadChip array, n=668). FINDINGS: We identified four CpGs and 28 regions associated with PAPE in the total population, 469 CpGs and 87 regions in male infants, and 150 CpGs and 66 regions in female infants. We validated 35% of the CpGs available. More than 30% of the identified CpGs were related to one (or more) birth outcome and most significant alterations were enriched for neural development, immunity, and metabolism related genes. The 28 regions identified for both sexes overlapped with imprinted genes (four genes), and were associated with neurodevelopment (nine genes), immune system (seven genes), and metabolism (five genes). Most associations were observed for the third trimester for female infants (134 of 150 CpGs), and throughout pregnancy (281 of 469 CpGs) and the first trimester (237 of 469 CpGs) for male infants. INTERPRETATION: These findings highlight the molecular pathways through which PAPE might affect child health in a widespread and sex-specific manner, identifying the genes involved in the major physiological functions of a developing child. Further studies are needed to elucidate whether these epigenetic changes persist and affect health later in life. FUNDING: French Agency for National Research, Fondation pour la Recherche Médicale, Fondation de France, and the Plan Cancer.

Ambient fine particulate matter and daily mortality: a comparative analysis of observed and estimated exposure in 347 cities.

BACKGROUND: Model-estimated air pollution exposure products have been widely used in epidemiological studies to assess the health risks of particulate matter with diameters of ≤2.5 µm (PM2.5). However, few studies have assessed the disparities in health effects between model-estimated and station-observed PM2.5 exposures. METHODS: We collected daily all-cause, respiratory and cardiovascular mortality data in 347 cities across 15 countries and regions worldwide based on the Multi-City Multi-Country collaborative research network. The station-observed PM2.5 data were obtained from official monitoring stations. The model-estimated global PM2.5 product was developed using a machine-learning approach. The associations between daily exposure to PM2.5 and mortality were evaluated using a two-stage analytical approach. RESULTS: We included 15.8 million all-cause, 1.5 million respiratory and 4.5 million cardiovascular deaths from 2000 to 2018. Short-term exposure to PM2.5 was associated with a relative risk increase (RRI) of mortality from both station-observed and model-estimated exposures. Every 10-µg/m3 increase in the 2-day moving average PM2.5 was associated with overall RRIs of 0.67% (95% CI: 0.49 to 0.85), 0.68% (95% CI: -0.03 to 1.39) and 0.45% (95% CI: 0.08 to 0.82) for all-cause, respiratory, and cardiovascular mortality based on station-observed PM2.5 and RRIs of 0.87% (95% CI: 0.68 to 1.06), 0.81% (95% CI: 0.08 to 1.55) and 0.71% (95% CI: 0.32 to 1.09) based on model-estimated exposure, respectively. CONCLUSIONS: Mortality risks associated with daily PM2.5 exposure were consistent for both station-observed and model-estimated exposures, suggesting the reliability and potential applicability of the global PM2.5 product in epidemiological studies.

Effects of extreme meteorological factors and high air pollutant concentrations on the incidence of hand, foot and mouth disease in Jining, China.

Background: The evidence on the effects of extreme meteorological conditions and high air pollution levels on incidence of hand, foot and mouth disease (HFMD) is limited. Moreover, results of the available studies are inconsistent. Further investigations are imperative to elucidate the specific issue. Methods: Data on the daily cases of HFMD, meteorological factors and air pollution were obtained from 2017 to 2022 in Jining City. We employed distributed lag nonlinear model (DLNM) incorporated with Poisson regression to explore the impacts of extreme meteorological conditions and air pollution on HFMD incidence. Results: We found that there were nonlinear relationships between temperature, wind speed, PM2.5, SO2, O3 and HFMD. The cumulative risk of extreme high temperature was higher at the 95th percentile (P95th) than at the 90th percentile(P90th), and the RR values for both reached their maximum at 10-day lag (P95th RR = 1.880 (1.261-2.804), P90th RR = 1.787 (1.244-2.569)), the hazardous effect of extreme low temperatures on HFMD is faster than that of extreme high temperatures. The cumulative effect of extreme low wind speeds reached its maximum at 14-day lag (P95th RR = 1.702 (1.389-2.085), P90th RR = 1.498(1.283-1.750)). The cumulative effect of PM2.5 concentration at the P90th was largest at 14-day lag (RR = 1.637 (1.069-2.506)), and the cumulative effect at the P95th was largest at 10-day lag (RR = 1.569 (1.021-2.411)). High SO2 concentration at the P95th at 14-day lag was associated with higher risk for HFMD (RR: 1.425 (1.001-2.030)). Conclusion: Our findings suggest that high temperature, low wind speed, and high concentrations of PM2.5 and SO2 are associated with an increased risk of HFMD. This study not only adds insights to the understanding of the impact of extreme meteorological conditions and high levels of air pollutants on HFMD incidence but also holds practical significance for the development and enhancement of an early warning system for HFMD.

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.

Comparison of numerical calculations and ALOHA modeling in consequence assessment of chlorine gas emissions from ethylene dichloride reactors.

Incidents involving chemical storage tanks in the petrochemical industry are significant events with severe consequences. Within the petrochemical industry, EDC is a sector that produces ethylene dichloride through the reaction of chlorine and ethylene. The present research was conducted to evaluate the consequences of chlorine gas released from the EDC reactor in a petrochemical industry in southern Iran. Data regarding reactor specifications were obtained from the factory's technical office, while climatic data was acquired from the Meteorological Organization. The consequences of chlorine gas release from the reactor were assessed in four predefined scenarios using numerical calculation methods and modeling with the ALOHA software. The numerical calculation method involved thermodynamic fluid path analysis, discharge coefficient calculations, and wind speed impact analysis. The hazard radius was determined based on the ERPG1-2-3 index. Results showed that in the scenario of chlorine gas release from EDC reactors, according to the ALOHA model, an increase in wind speed from 3 to 7 m/h led to an expanded dispersion radius. At a radius of 700 m from the reactor, the maximum outdoor concentration reached 3.12 ppm, decreasing to 2.27 ppm at 800 m and further to 1.53 ppm at 1000 m. The comparison of numerical calculations and modeling using the ALOHA software indicates the desirable conformity of the results with each other. The R2 coefficient for evaluating the conformity of the results was 0.9964, indicating the desired efficiency of the model in evaluating the consequences of the release of toxic gasses from the EDC tank. The results of this research can be useful in designing the site and emergency response plan.

China's Fossil Fuel CO2 Emissions Estimated Using Surface Observations of Coemitted NO2.

Accurate estimates of fossil fuel CO2 (FFCO2) emissions are of great importance for climate prediction and mitigation regulations but remain a significant challenge for accounting methods relying on economic statistics and emission factors. In this study, we employed a regional data assimilation framework to assimilate in situ NO2 observations, allowing us to combine observation-constrained NOx emissions coemitted with FFCO2 and grid-specific CO2-to-NOx emission ratios to infer the daily FFCO2 emissions over China. The estimated national total for 2016 was 11.4 PgCO2·yr-1, with an uncertainty (1σ) of 1.5 PgCO2·yr-1 that accounted for errors associated with atmospheric transport, inversion framework parameters, and CO2-to-NOx emission ratios. Our findings indicated that widely used "bottom-up" emission inventories generally ignore numerous activity level statistics of FFCO2 related to energy industries and power plants in western China, whereas the inventories are significantly overestimated in developed regions and key urban areas owing to exaggerated emission factors and inexact spatial disaggregation. The optimized FFCO2 estimate exhibited more distinct seasonality with a significant increase in emissions in winter. These findings advance our understanding of the spatiotemporal regime of FFCO2 emissions in China.

Passive biomonitoring of airborne microplastics using lichens: A comparison between urban, natural and protected environments.

Currently, natural and urban ecosystems are affected by different types of atmospheric deposition, which can compromise the balance of the environment. Plastic pollution represents one of the major threats for biota, including lichens. Epiphytic lichens have value as bioindicators of environmental pollution, climate change, and anthropic impacts. In this study, we aim to investigate the lichen bioaccumulation of airborne microplastics along an anthropogenic pollution gradient. We sampled lichens from the Genera Cladonia and Xanthoria to highlight the effectiveness of lichens as tools for passive biomonitoring of microplastics. We chose three sites, a "natural site" in Altipiani di Arcinazzo, a "protected site" in Castelporziano Presidential estate and an "urban site" in the centre of Rome. Overall, we sampled 90 lichens, observed for external plastic entrapment, melt in oxygen peroxide and analysed for plastic entrapment. To validate the method, we calculated recovery rates of microplastics in lichen. Particularly, 253 MPs particles were detected across the 90 lichen samples: 97 % were fibers, and 3 % were fragments. A gradient in the number of microplastic fibers across the sites emerged, with increasing accumulation of microplastics from the natural site (n = 58) to the urban site (n = 116), with a direct relationship between the length and abundance of airborne microplastic fibers. Moreover, we detected the first evidences of airborne mesoplastics entrapped by lichens. On average, the natural site experienced the shortest fibre length and the centre of Rome the longest. No differences in microplastics accumulation emerged from the two genera. Our results indicated that lichens can effectively be used for passive biomonitoring of microplastic deposition. In this scenario, the role of lichens in entrapping microplastics and protecting pristine areas must be investigated. Furthermore, considering the impact that airborne microplastics can have on human health and the effectiveness of lichens as airborne microplastic bioindicators, their use is encouraged.

Environmental Justice and Systems Analysis for Air Quality Planning in the Port of Oakland in California.

Many frontline communities experience adverse health impacts from living in proximity to high-polluting industrial sources. Securing environmental justice requires, in part, a comprehensive set of quantitative indicators. We incorporate environmental justice and life-cycle thinking into air quality planning to assess fine particulate matter (PM2.5) exposure and monetized damages from operating and maintaining the Port of Oakland, a major multimodal marine port located in the historically marginalized West Oakland community in the San Francisco Bay Area. The exposure domain for the assessment is the entire San Francisco Bay Area, a home to more than 7.5 million people. Of the more than 14 sources included in the emissions inventory, emissions from large container ships, or ocean-going vessels (OGVs), dominate the PM2.5 intake, and supply chain sources (material production and delivery, fuel production) represent between 3.5% and 7.5% of annual intake. Exposure damages, which model the costs from excess mortalities resulting from exposure from the study's emission sources, range from USD 100 to 270 million per annum. Variations in damages are due to the use of different concentration-response relationships, hazard ratios, and Port resurfacing area assumptions. Racial and income-based exposure disparities are stark. The Black population and people within the lowest income quintile are 2.2 and 1.9 times more disproportionately exposed, respectively, to the Port's pollution sources relative to the general population. Mitigation efforts focused on electrifying in-port trucking operations yield modest reductions (3.5%) compared to strategies that prioritize emission reductions from OGVs and commercial harbor craft operations (8.7-55%). Our recommendations emphasize that a systems-based approach is critical for identifying all relevant emission sources and mitigation strategies for improving equity in civil infrastructure systems.