Major Air Pollutants Research Articles

Analysing ML Models for AQI Prediction using Meteorological Data

Air is essential for every living being to survive. The modernization and industrialization have led to the rise of pollutants which affect the quality of air directly. Predicting changes in air pollutant concentrations due to human and nature drivers is critical and challenging, particularly in areas with scant data inputs and high variability of parameters. The air quality index (AQI) is an index for reporting air quality on a daily basis. It is a measure of how air pollution affects one's health within a short time period. The purpose of the AQI is to help people know how the local air quality impacts their health. The Environmental Protection Agency (EPA) calculates the AQI for five major air pollutants, for which national air quality standards have been established to safeguard public health. Every country has its own method of calculating the Air Quality Index and the rules for Air Quality Buckets. According to the Central pollution control board, Ministry of Environment, Forests, and climate change of India, the AQI is divided into 6 buckets with the minimum and maximum AQI being 0 and 500 respectively. The buckets are Good, Satisfactory, Moderate, Poor, Very Poor and Severe.

Intrauterine and early postnatal exposure to air pollution associated with childhood allergic rhinitis

BackgroundDespite mounting evidence linking allergic rhinitis (AR) to air pollution, it remains unclear which major air pollutant(s) and critical window(s) of exposure play important roles in children's AR. ObjectiveTo examine the effects of intrauterine and early postnatal exposure to outdoor air pollution on children with doctor-diagnosed allergic rhinitis (DDAR). MethodsA retrospective cohort study involving 8689 kindergarten children was conducted in Changsha, China, from 2019 to 2020. A questionnaire survey was conducted to collect information on the health status of children and their family members, as well as their living habits and home environment. Personal exposure to daily outdoor air pollutants (PM2.5, PM2.5-10, PM10, SO2, NO2, and CO) was estimated during 40 gestational weeks, three trimesters, the entire pregnancy, and the first year after birth. Multiple logistic regression models were used to assess the associations between air pollution and children's DDAR. ResultsChildren's DDAR was associated with intrauterine CO exposure, with adjusted ORs (95% CI) of 1.18 (1.03–1.34) for each IQR increase in CO exposure. The second and third trimesters were critical windows for PM2.5 and CO exposure in relation to DDAR. Furthermore, early postnatal exposure to PM2.5-10 and PM10 in first year of life was associated with DDAR development, with adjusted ORs (95% CI) of 1.11 (1.01–1.22) and 1.27 (1.09, 1.47). The entire pregnancy and the first year of life were critical windows for CO and PM10 exposure. Some children were predisposed to DDAR risk due to exposure to traffic-related air pollution (TRAP). ConclusionOur findings support the hypothesis of “fetal origin of allergic rhinitis” by demonstrating that intrauterine and early postnatal exposure to air pollution plays an important role in children's DDAR.

Distinct urban-rural gradients of air NO2 and SO2 concentrations in response to emission reductions during 2015–2022 in Beijing, China

Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are two major air pollutants in urban environment. Emission reduction policies have thus been implemented to improve urban air quality, especially in the metropolises. However, it remains unclear whether the air concentrations of NO2 and SO2 in and around large cities follow a same spatial pattern and how their characteristics change over time in response to the emission reductions. Using ground-based monitoring datasets of air NO2 and SO2 concentrations in Beijing, China, we tested the hypothesis of urban air pollutant islands and evaluated their seasonal and inter-annual variations during 2015–2022. The results showed that air NO2 concentrations increased significantly towards the urban core, being in line with the hypothesis of urban air pollutant island, while air SO2 concentrations showed no such spatial patterns. The urban air NO2 island varied seasonally, with larger radius and higher air NO2 concentrations in spring and winter. In response to the emission reduction, the annual mean radius of the urban air NO2 island showed a rapid decrease from 45.8 km to zero km during the study period. The annual mean air NO2 concentration at the urban core showed a linear decrease at a rate of 4.5 μg m−3 yr−1. In contrast, air SO2 concentration decreased nonlinearly over time and showed a legacy in comparison to the emission reduction. Our findings suggest different urban-rural gradients of air NO2 and SO2 concentrations and highlight their distinct responses to the regional reductions of anthropogenic emissions.

Regional Representativeness Analysis of Ground-Monitoring PM2.5 Concentration Based on Satellite Remote Sensing Imagery and Machine Learning Techniques

The issue of urban air quality in China has become increasingly significant due to industrialization and rapid urbanization. Although PM2.5 is the major air pollutant in most cities of northern China and has a direct negative impact on human health, there is a problem of under-representativeness at Chinese monitoring stations. In some cities, due to the relatively fewer national control stations and the fact that the stations are located closer to pollution sources, under the current assessment system, the monitoring data are not sufficient for the fairness of air quality assessment in different cities. In this article, the multispectral data of Landsat 8 data, air quality data, and meteorological data from ground monitoring stations have been integrated together and imported to different PM2.5-estimation models established based on the multi-layer back propagation neural network (MLBPN), support vector regression (SVR), and random forest (RF), respectively. According to the evaluation indices of R2, RMSE, and ME, the estimation model based on the MLBPN revealed the best PM2.5 estimation results and was therefore employed for the regional representativeness analysis in the study area of Xi’an, Shaanxi, China. The annual average PM2.5 concentration in the study area is depicted after error correction using Kriging interpolation, which can be further used to evaluate and analyze the representativeness of monitoring stations in Xi’an. By calculating the difference between the actual station annual average and the annual average of estimated PM2.5 concentration in the whole region, it can be found that the regional annual average value of PM2.5 in Xi’an is overestimated. To sum up, this article proposes a feasible method for the spatial positioning of the air quality monitoring stations to be established.

Assessment of Emission Reduction Effect of Major Air Pollution Control Measures on PM2.5 Concentrations During 13th Five-Year Period in Tianjin

The emission reduction effect of major air pollution control measures on PM2.5 concentrations was assessed using air quality simulations based on the calculation data of emission reductions from different air pollution control measures and the high spatiotemporal resolution online monitoring data of PM2.5 during the 13th Five-Year Period in Tianjin. The results showed that the total emission reductions of SO2, NOx, VOCs, and PM2.5 from 2015 to 2020 were 4.77×104, 6.20×104, 5.37×104, and 3.53×104 t, respectively. SO2 emission reduction was mainly due to the prevention of process pollution, loose coal combustion, and thermal power. NOx emission reduction was mainly due to the prevention of process pollution, thermal power, and steel industry. VOCs emission reduction was mainly due to prevention of process pollution. PM2.5 emission reduction was mainly due to the prevention of process pollution, loose coal combustion, and the steel industry. The concentrations, pollution days, and heavy pollution days of PM2.5 decreased significantly from 2015 to 2020 by 31.4%, 51.2%, and 60.0% compared to those in 2015, respectively. The concentrations and pollution days of PM2.5 decreased slowly in the later stage (from 2018 to 2020)as compared with those in the early stage (from 2015 to 2017), and the days of heavy pollution remained for approximately 10 days. The results of air quality simulations showed that meteorological conditions contributed one-third to the reduction in PM2.5 concentrations, and the emission reductions of major air pollution control measures contributed two-thirds to the reduction in PM2.5 concentrations. For all air pollution control measures from 2015 to 2020, PM2.5 concentrations were reduced by the prevention of process pollution, loose coal combustion, the steel industry, and thermal power by 2.66, 2.18, 1.70, and 0.51 μg·m-3, respectively, accounting for 18.3%, 15.0%, 11.7%, and 3.5% of PM2.5 concentration reductions. In order to promote the continuous improvement in PM2.5 concentrations during the 14th Five-Year Plan period, under the total coal consumption control and the goal of "peaking carbon dioxide emissions and achieving carbon neutrality," Tianjin should continue to optimize and adjust the coal structure and further promote the coal consumption to the power industry with an advanced pollution control level. At the same time, it is necessary to further improve the emission performance of industrial sources in the whole process, taking environmental capacity as the constraint; design the technical route for industrial optimization, adjustment, transformation, and upgrading; and optimize the allocation of environmental capacity resources. Additionally, the orderly development model for key industries with limited environmental capacity should be proposed, and clean upgrading, transformation, and green development should be guided for enterprises.

Association between ambient air pollutants and upper respiratory tract infection and pneumonia disease burden in Thailand from 2000 to 2022: a high frequency ecological analysis

BackgroundA pertinent risk factor of upper respiratory tract infections (URTIs) and pneumonia is the exposure to major ambient air pollutants, with short term exposures to different air pollutants being shown to exacerbate several respiratory conditions.MethodsHere, using disease surveillance data comprising of reported disease case counts at the province level, high frequency ambient air pollutant and climate data in Thailand, we delineated the association between ambient air pollution and URTI/Pneumonia burden in Thailand from 2000 – 2022. We developed mixed-data sampling methods and estimation strategies to account for the high frequency nature of ambient air pollutant concentration data. This was used to evaluate the effects past concentrations of fine particulate matter (PM2.5), sulphur dioxide (SO2), and carbon monoxide (CO) and the number of disease case count, after controlling for the confounding meteorological and disease factors.ResultsAcross provinces, we found that past increases in CO, SO2, and PM2.5 concentration were associated to changes in URTI and pneumonia case counts, but the direction of their association mixed. The contributive burden of past ambient air pollutants on contemporaneous disease burden was also found to be larger than meteorological factors, and comparable to that of disease related factors.ConclusionsBy developing a novel statistical methodology, we prevented subjective variable selection and discretization bias to detect associations, and provided a robust estimate on the effect of ambient air pollutants on URTI and pneumonia burden over a large spatial scale.

Low-carbon energy policies benefit climate change mitigation and air pollutant reduction in megacities: An empirical examination of Shenzhen, China

The low-carbon transformation of energy system has great significance for megacities to mitigate climate change, which brings co-benefits to improve urban air quality. Taking China's megacity, Shenzhen, as an example, this study examines the potential of wide-ranging energy policies in urban GHG emission reduction and the associated synergistic effect on decreasing major air pollutant emissions. Based on the low emissions analysis platform (LEAP) model, the major results show that an effective implementation of newly emerging energy policies could help cap GHG emissions of Shenzhen in 2025 and nearly halve them by 2035, which would contribute substantially to reducing urban air pollutant emissions. At the sectoral level, the synergistic effect of emission reduction would be the strongest in the transportation sector, followed by the electricity and manufacturing sectors, while it is not significant in the building sector. Moreover, all policies on energy efficiency improvement and demand management that reduce fossil energy consumption show synergistic effects on decreasing air pollutants, while policies on energy structural optimization show differentiated impacts across SO2, NOx, VOCs, and PM2.5. Urban managers should prioritize energy policies with strong synergistic effects and specifically promote the wide application of rooftop PV system and deep electrification of road transportation.

The metal ion in single-atom NO reduction electrocatalysts dictates product selectivity

Nitric oxide is a major air pollutant associated with environmental problems, including acid rain, smog, and depletion of the ozone layer. In this issue of Chem Catalysis, Zhang, Li, and co-workers develop mono-dispersed single-atom transition-metal (Fe, Co, and Ni) electrocatalysts, where the metal ion dictates product selectivity, to reduce NO.

Performance and Mechanism of Chlorine Dioxide on BTEX Removal in Liquid and Indoor Air.

With the development of the chemical industry, benzene, toluene, ethylbenzene, and xylene (BTEX) have gradually become the major indoor air pollutants. Various gas treatment techniques are widely used to prevent the physical and mental health hazards of BTEX in semi-enclosed spaces. Chlorine dioxide (ClO2) is an alternative to chlorine as a secondary disinfectant with a strong oxidation ability, a wide range of action, and no carcinogenic effects. In addition, ClO2 has a unique permeability which allows it to eliminate volatile contaminants from the source. However, little attention has been paid to the removal of BTEX by ClO2, due to the difficulty of removing BTEX in semi-enclosed areas and the lack of testing methods for the reaction intermediates. Therefore, this study explored the performance of ClO2 advanced oxidation technology on both liquid and gaseous benzene, toluene, o-xylene, and m-xylene. The results showed that ClO2 was efficient in the removal of BTEX. The byproducts were detected by gas chromatography-mass spectrometry (GC-MS) and the reaction mechanism was speculated using the ab initio molecular orbital calculations method. The results demonstrated that ClO2 could remove the BTEX from the water and the air without causing secondary pollution.

Convolutional neural network approach for reduction of nitrogen oxides emissions from pulverized coal-fired boiler in a power plant for sustainable environment

Coal-fired power plants are the main electric power source across many countries and cause major air pollution problems such as acid rain, smog, ozone depletion, and global warming. According to the best of the authors' knowledge, this is by far the first study that proposed 1-dimensional Convolutional Neural Network (1d-CNN) in combination with teaching learning self-study optimization (TLSO) algorithm for NOx emissions reduction by optimizing process input variables in a pulverized coal-fired power plant. The proposed model reduced the NOx emissions by 50.9%. In addition, the reduction experiment resulted in the early convergence superiority of the TSLO (130 s, 30th iteration) compared to genetic algorithm and Bayesian optimization. Based on the result, it is evident that combination of computationally inexpensive 1d-CNN and relatively fast converging TLSO could help process engineers reduce NOx emissions, which could ultimately contribute towards the goal of a sustainable environment.

Indoor Volatile Organic Compounds: Concentration Characteristics and Health Risk Analysis on a University Campus.

Volatile organic compounds (VOCs) are major indoor air pollutants that contain several toxic substances. However, there are few studies on health risk assessments of indoor VOCs in China. This study aimed to determine the concentration characteristics of VOCs on college campuses by collecting VOC samples from different locations on campus during different seasons combined with the exposure times of college students in each location obtained from a questionnaire survey to assess the possible health risks. The highest total VOC concentration (254 ± 101 µg/m3) was in the dormitory. The seasonal variation of TVOC concentrations was related to the variation of emission sources in addition to temperature. Health risk assessments of VOCs were evaluated using non-carcinogenic and carcinogenic risk values, represented by hazard quotient (HQ) and lifetime cancer risk (LCR), respectively. The non-carcinogenic risks at all sampling sites were within the safe range (HQ < 1). Dormitories had the highest carcinogenic risk, whereas the carcinogenic risk in the other three places was low (with LCR < 1.0 × 10-6). Moreover, 1,2-dichloroethane was identified as a possible carcinogenic risk substance in the dormitory due to its high LCR (1.95 × 10-6). This study provides basic data on health risks in different locations on campus and a basis for formulating measures to improve people's living environments.

An enhanced loss function in deep learning model to predict PM2.5 in India

Fine particulate matter (PM2.5) is one of the major air pollutants and is an important parameter for measuring air quality levels. High concentrations of PM2.5 show its impact on human health, the environment, and climate change. An accurate prediction of fine particulate matter (PM2.5) is significant to air pollution detection, environmental management, human health, and social development. The primary approach is to boost the forecast performance by reducing the error in the deep learning model. So, there is a need to propose an enhanced loss function (ELF) to decrease the error and improve the accurate prediction of daily PM2.5 concentrations. This paper proposes the ELF in CTLSTM (Chi-Square test Long Short Term Memory) to improve the PM2.5 forecast. The ELF in the CTLSTM model gives more accurate results than the standard forecast models and other state-of-the-art deep learning techniques. The proposed ELFCTLSTM reduces the prediction error of by a maximum of 10 to 25 percent than the state-of-the-art deep learning models.

Sex disparity in cognitive aging related to later-life exposure to ambient air pollution

BackgroundSex difference in the association between ambient air pollution and cognitive aging remained unclear. This current study aimed to assess the impacts of long-term exposure to major air pollutants on cognitive performance among Chinese middle-aged and older adults, and explore whether these associations could be modified by sex. MethodsBy deriving longitudinal data from four waves of the China Health and Retirement Longitudinal Study, we included 13,507 participants aged 45+ years who had at least two cognitive tests recorded from 2011 to 2018. We used a standardized questionnaire consisting of five domain-specific functions to measure global cognitive score. Based on well-validated spatiotemporal datasets, annual average concentrations of ambient fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) were assigned to each enrollee at prefectural city level and at the simulated residential addresses through Monte Carlo simulation approach. Linear mixed-effects models were applied to assess the impacts of major air pollutants on cognitive function with multiple adjustments. Sex-stratified analyses were performed to examine the potential effect modification on these associations. To enhance the interpretability of our results, we also compared the estimated effects of air pollutants with the effect of aging on cognitive function. ResultsWe evaluated 38,950 records of cognitive function tests, of which 44.0% were from women. An increase of 10-μg/m3 in PM2.5, NO2 and O3 exposure were associated with 0.36 (95% confidence interval: −0.40, −0.31), 0.51 (−0.60, −0.43) and 0.26 (−0.47, −0.06) points decline in global cognitive score, respectively, equivalent to the effect of aging by 2.8–5.0 years. Sex-stratified analyses suggested significantly greater cognitive impairment associated with air pollutants in women than men. We found reversely J-shaped concentration-response relationships between ambient air pollutants and cognitive decline in both sexes. Main findings on sex-specific associations were robust to the adjustments for covariates, inclusion criteria, and co-pollutant analyses, as well as sensitivity analyses based on simulation-based exposure for PM2.5 and NO2. ConclusionsLater-life exposure to ambient air pollution may accelerate cognitive aging of middle-aged and older adults, suggesting significant sex disparity with higher vulnerability in women.

Inventory of PCDD/Fs and Fly Ash Characteristics during the e-Waste and Municipal Solid Waste Co-Incineration Process

In the face of a challenging situation for yearly e-waste (EW) increment and disposition, EW co-incineration (EWC) with municipal solid waste (MSW) is a promising solution whose emission characteristics remain unclear. This study is conducted in a 400 tons/day MSW incineration (MSWI) plant to characterize polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) and fly ash (FA) properties. Analytical methods include high-resolution gas chromatography/high-resolution mass spectrometry (HRGC/HRMS) for PCDD/F determination, as well as X-ray diffraction (XRD), X-ray fluorescence (XRF), ion chromatography (IC), and leaching toxicity test for FA. The results indicate that toxic PCDD/F amounts are doubled by EWC in both primary exhaust gas (EG) and FA with international toxic equivalent quantity (I-TEQ) concentrations of 19.00 ± 6.32 ng I-TEQ/Nm3 and 1.33 ± 0.56 ng I-TEQ/g. After the clean-up of the air pollution control system (APCS), emitted PCDD/Fs in solid and gas phases, as well as major air pollutants (CO, NOx, SO2, HCl, and particulate matter), are permissive within the limit values of national standards GB 18485-2014 and GB 16889-2008. Distribution patterns for 136 PCDD/F isomers are analogous in both MSWI and EWC conditions. De novo synthesis is always the major formation route for both EWC and MSWI conditions, while dibenzodioxin/dibenzofuran (DD/DF) chlorination also contributes. The generation of 136 PCDD/Fs is enhanced by EWC, and the correlation coefficients of Cl and Cu in the fuel with the generation of 136 PCDD/F isomers are found to be R = 0.92 and 0.88, respectively. The main components in FA from MSWI and EWC differ less, both containing CaO, SiO2, and Al2O3. However, Cl and Cu contents, as well as leaching toxicity in FA are elevated by EW. Heavy metals, except for Cd, Ni, and Pb, meet the requirement of the standard.

Development of an ecophysiology module in the GEOS-Chem chemical transport model version 12.2.0 to represent biosphere–atmosphere fluxes relevant for ozone air quality

Abstract. Ground-level ozone (O3) is a major air pollutant that adversely affects human health and ecosystem productivity. Removal of tropospheric O3 by plant stomatal uptake can in turn cause damage to plant tissues with ramifications for ecosystem and crop health. In many atmospheric and land surface models, the functionality of stomata opening is represented by a bulk stomatal conductance, which is often semi-empirically parameterized and highly fitted to historical observations. A lack of mechanistic linkage to ecophysiological processes such as photosynthesis may render models inadequate to represent plant-mediated responses of atmospheric chemistry to long-term changes in CO2, climate, and short-lived air pollutant concentrations. A new ecophysiology module was thus developed to mechanistically simulate land−atmosphere exchange of important gas species in GEOS-Chem, a chemical transport model widely used in atmospheric chemistry studies. The implementation not only allows for dry deposition to be coupled with plant ecophysiology but also enables plant and crop productivity and functions to respond dynamically to atmospheric chemical changes. We conduct simulations to evaluate the effects of the ecophysiology module on simulated dry deposition velocity and concentration of surface O3 against an observation-derived dataset known as SynFlux. Our estimated stomatal conductance and dry deposition velocity of O3 are close to SynFlux with root-mean-squared errors (RMSEs) below 0.3 cm s−1 across different plant functional types (PFTs), despite an overall positive bias in surface O3 concentration (by up to 16 ppbv). Representing ecophysiology was found to reduce the simulated biases in deposition fluxes from the prior model but worsen the positive biases in simulated O3 concentrations. The increase in positive concentration biases is mostly attributable to the ecophysiology-based stomatal conductance being generally smaller (and closer to SynFlux values) than that estimated by the prior semi-empirical formulation, calling for further improvements in non-stomatal depositional and non-depositional processes relevant for O3 simulations. The estimated global O3 deposition flux is 864 Tg O3 yr−1 with GEOS-Chem, and the new module decreases this estimate by 92 Tg O3 yr−1. Estimated global gross primary production (GPP) without O3 damage is 119 Pg C yr−1. O3-induced reduction in GPP is 4.2 Pg C yr−1 (3.5 %). An elevated CO2 scenario (580 ppm) yields higher global GPP (+16.8 %) and lower global O3 depositional sink (−3.3 %). Global isoprene emission simulated with a photosynthesis-based scheme is 317.9 Tg C yr−1, which is 31.2 Tg C yr−1 (−8.9 %) less than that calculated using the MEGAN (Model of Emissions of Gases and Aerosols from Nature) emission algorithm. This new model development dynamically represents the two-way interactions between vegetation and air pollutants and thus provides a unique capability in evaluating vegetation-mediated processes and feedbacks that can shape atmospheric chemistry and air quality, as well as pollutant impacts on vegetation health, especially for any timescales shorter than the multidecadal timescale.

Assessment of NO2 concentrations over industrial state Jharkhand, at the time frame of pre, concurrent, and post-COVID-19 lockdown along with the meteorological behaviour: an overview from satellite and ground approaches

Burning of fossil fuels in the form of coal or gasoline in thermal power plants, industries, and automobiles is a prime source of nitrogen dioxide (NO2), a major air pollutant causing health problems. In this paper, spatio-temporal unevenness of NO2 concentrations via both spaceborne Sentinel-5P and ground-based in situ data have been studied for the period of 2017-2021. Annual and seasonal distribution of TROPOMI-NO2 depict consistency over the Jharkhand region, highlighting six hotspot regions. As compared to 2019, a notable dip of 11% in the spatial annual average TROPOMI-NO2 was achieved in 2020, which were elevated again by 22% in 2021 as the lockdown gradually goes out of the picture. Among eight ground-monitoring stations, Tata and Golmuri stations always displayed a higher level of TROPOMI-NO2 ranges up to 15.2 ×1015molecules.cm-2 and 16.9 ×1015molecules.cm-2 respectively, as being located in the highly industrialised district of Jamshedpur. A big percentage reduction of up to 30% in TROPOMI-NO2 has been reported in Jharia and Bastacola stations in Dhanbad in the lockdown phase of 2020 compared to 2019. Good agreement between TROPOMI-NO2 and surface-NO2 has been achieved with R = 0.8 and R = 0.71 during winter and post-monsoon respectively. Among four meteorological parameters, TROPOMI-NO2 was majorly found to be influenced by precipitation, having R = 0.6-0.8 for almost all stations. More advanced satellite algorithms and ground-based data may be used to estimate NO2 in places where monitoring facilities are limited and thus can help in air pollution control policy.

Status of Ambient Air Pollution in Different States of India during 1990-2015

Industrial activity, high vehicle density, and expanding industrial and domestic heating are all contributing to air pollution, which is becoming an increasingly severe issue across the globe as urban population and human activity soar. This is especially true in major cities, where emissions from vehicles on the road expose a substantial portion of the population to harmful levels of air pollution. This research paper focuses on which states or locations of India suffered from ambient air pollution. In this paper, the air pollutants considered are Sulphur Dioxide (SO2), Nitrogen dioxide NO2, Particulate Matter (PM2.5), and Respirable Suspended Particulate Matter (RSPM), and Suspended Particulate Matter (SPM). The findings suggest that pollution for all criteria pollutants is more prevalent in industrial areas across India, followed by residential areas. The government of India should take steps to develop pollution control regulations in impacted areas to reduce air pollution and provide people with clean air. Both Uttar Pradesh and Delhi, which are among the most polluted states in the country, have major air pollution problems.

Impact of short- and long-term exposure to air pollution on lipid profile in adults aged 20 to 69 years in Tehran Province, Iran

There are inconsistent findings concerning the impact of air pollution on different lipid measures and there is a lack of data for the highly polluted Middle East and North Africa (MENA) region. To assess the association between short-term exposure to five major air pollutants including PM10, NO2, SO2, O3, and CO, and lipid profiles as well as long-term exposure to these pollutants and incident dyslipidemia. A total of 5,821 participants (3,387 women) aged 20–69 years at baseline were followed from 2001 to 2018, every three years. The mixed-effects model evaluated the impact of air pollutants on blood lipids including TC, TG, HDL-C, LDL-C, and non-HDL-C. The proportional hazards Weibull model estimated the long-term impacts of air pollutants on incident dyslipidemia. Applying the Bonferroni correction, the significance level of 0.0014 (0.05/35) and 0.0033 (0.05/15) was set for short- and long-term scenarios, respectively. In the short term, SO2 and CO had consistent unfavorable effects on lipid profiles, including TC, TG, LDL-C, non-HDL-C, and HDL-C (only for CO). Moreover, short-term exposure to O3 and NO2 had negative associations with TG, LDL-C, HDL-C, and TC (only NO2) and positive associations with HDL-C. Generally, in a long-term manner, CO was associated with a higher incidence of high TC, high TG, and low HDL-C; NO2 and O3 were associated with a lower incidence of high TG and low HDL-C; 1- and 2-year exposure to PM10 was associated with lower incidence of low HDL-C and 3-year exposure to PM10 was associated with lower high TC and high non-HDL-C. In conclusion, given the robust unfavorable impact of CO on lipid profiles in both short- and long-term periods, more interventions should be conducted to reduce the impact of CO exposure.

Emission characteristics of condensable particulate matter (CPM) from FCC flue gas

Particulate matter (PM) as a major air pollutant, generally includes filterable particulate matter (FPM) and condensable particulate matter (CPM). CPM has gradually attracted widespread attention recently, due to its increasing proportion in total PM emissions. Fluid catalytic cracking (FCC) units, the main emission source in refineries, mostly use wet flue gas desulfurization (WFGD), which will produce a large amount of CPM. However, CPM emission and composition of FCC units are actually unclear. In this work, we aimed to understand the emission characteristics of CPM in FCC flue gas and provide some potential control strategies. Here, the stack tests of three typical FCC units were conducted to monitor FPM and CPM, and the field monitoring FPM results are higher than the concentration provided by Continuous Emission Monitoring System (CEMS). The emission of CPM is at a high-level concentration from 28.88 to 86.17 mg/Nm3, divided into inorganic fraction and organic fraction. The inorganic fraction is mainly composed in CPM, where water-soluble ions including SO42−, Na+, NH4+, NO3−, CN−, Cl−, and F−, are the major contributors. Moreover, a variety of organic compounds are detected as qualitative analysis of organic fraction in CPM, which can be roughly classified into alkanes, esters, aromatics, and others. Finally, on the basis of the understanding of the characteristics of CPM, we have proposed two strategies for CPM control. This work is expected to advance CPM emission regulation and control in FCC units.

Mapping nighttime PM2.5 concentrations in Nanjing, China based on NPP/VIIRS nighttime light data

Fine inhalable particulate matter (PM2.5) is one of the major air pollutants that affect human health and the environment. Detailed knowledge of the spatial distribution of PM2.5 is meaningful for the prevention and control of air pollution. Satellite remote sensing has become an effective way to observe PM2.5 concentrations. However, most studies have focused on mapping PM2.5 concentrations from satellite-derived daytime aerosol optical depth (AOD), which cannot effectively depict the nighttime atmospheric environment. This paper aims to develop a method to derive nighttime PM2.5 concentrations in Nanjing, China, using the National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light remote sensing data during September–December 2020. The relationship between the satellite at-sensor radiance, PM2.5 concentrations and other environmental factors was first explored based on the nighttime radiative transfer equation. Taking into account the pixel direct radiation and the background scattered radiation, the spatial independent variables for estimating nighttime PM2.5 were determined. Five machine learning algorithms and multiple linear regression (MLR) were employed to develop models to estimate nighttime PM2.5 concentrations. The results showed that the MLR model had obviously lower accuracy than the machine learning models, and the RF model outperformed the other models, with a coefficient of determination (R2) of 0.81 and a mean absolute error (MAE) of 7.85 μg·m−3. Then, the developed model was applied to map the nighttime PM2.5 concentrations over Nanjing, which well characterized the nighttime atmospheric environment at a fine resolution. This paper proposes a method to map nighttime PM2.5 concentrations from nighttime light remote sensing data and provides references for monitoring nighttime atmospheric environments in other regions.