Estimate Air Pollution Research Articles

Impacts of distinct travel behaviors on potential air pollution exposure measurement error

Prior studies typically estimate air pollution exposure at places of residence only; however, people are likely to conduct daily activities at out-of-home locations. Overlooking human mobility in exposure estimation can introduce measurement error in health analyses and may lead to ineffective public policies. We simulated travel mobility patterns for 100,784 residents of Los Angeles County and their exposure to outdoor PM2.5 on a typical weekday. We used K-means clustering via principal components analysis (PCA) to subdivide subjects into seven travel behavior clusters and compared their mobility-based dynamic PM2.5 exposures to their residence-based static exposure using our previously developed spatiotemporal model. Travel behavior correlated with exposure to outdoor PM2.5 and with the degree of PM2.5 exposure measurement error. Workers with long commuting distance (and highest income) had the lowest dynamic PM2.5 exposure (8.1 μg/m3), while workers who relied on public transit (and were generally lower income) had the highest exposure (8.8 μg/m3). In general, lower income individuals lived in more polluted neighborhoods and took longer duration (but shorter distance) commutes to work locations with similarly elevated PM2.5 concentrations, while higher income individuals took longer distance commutes (similar duration) from less polluted residential to more polluted occupational neighborhoods. “Worker” and frequent traveler clusters with highest mobility had the highest exposure measurement error, and the non-worker “stay-at-home” cluster and student clusters had the lowest. The extent of exposure measurement error generally correlated with pollution levels at the residential location and distance (less so duration) of travel away from the residential neighborhood. Our results contribute to the literature on effects of travel behavior on exposure measurement error. The generated activity clusters combined with sociodemographic information provide additional information for researchers to understand which groups of people may be more challenging to model or predict their exposures accurately.

Estimation of Air Pollutants Along with Meteorological Parameters and Study of Their Impact on Human Health

The research studies have shown that air pollution has been well thought-out as the main warning to health globally. It requests the participation of researchers from time to time to address and revive the problem sincerely. In India concentration of air pollutants has become a serious issue. In most Indian cities, the level of air pollutants is above the prescribed level as per the CPCB. Gwalior City of Madhya Pradesh (India) is at the entrance of speedy urbanisation and industrialisation which causes the worsening air quality of the city. As per the reports of 2015, Gwalior is listed among the top polluted city in India. To address this problem an investigation of air pollutant concentration was carried out at three stations in Gwalior. In this study SO2, NO2 and RSPM and SPM were collected over three stations categorised as residential, industrial and commercial and estimation was done by chemical methods. Meteorological parameters like temperature and relative humidity were also recorded during the sampling period. Seasonal variations of these pollutants have been analysed and noted. From the study, the concentrations of the pollutants were observed to be high in summer and winter than in monsoon and post-monsoon seasons. In this study, it was noticed that the RSPM and SPM levels at all selected sites exceed the prescribed limits. While the level of gaseous SO2 and NO2 are observed to remain under prescribed limits. The main objective of this study was to know the quality of air in Gwalior and study its health impacts as the city has been selected as one of the hundred Indian cities that can be developed into a smart city under PM Narendra Modi’s flagship Smart Cities Mission.

Understanding the Interplay between Air Pollution, Biological Variables, and Major Depressive Disorder: Rationale and Study Protocol of the DeprAir Study

Major depressive disorder (MDD) is a serious and disabling condition, whose etiological mechanisms are not fully understood. The aim of the DeprAir study is to verify the hypothesis that air pollution exposure may exacerbate neuroinflammation with consequent alterations in DNA methylation of genes involved in circadian rhythms and hormonal dysregulation, resulting in the worsening of depressive symptoms. The study population consists of 420 depressed patients accessing the psychiatry unit of the Policlinico Hospital (Milan, Italy), from September 2020 to December 2022. Data collection is still ongoing for about 100 subjects. For each participant demographic and lifestyle information, depression history and characteristics, as well as blood samples, were collected. MDD severity was assessed through five rating scales commonly used in clinical practice to assess the severity of affective symptoms. Exposure to particulate and gaseous air pollutants is assigned to each subject using both air pollution monitoring station measurements and estimates derived from a chemical transport model. DeprAir is the first study investigating in a comprehensive picture whether air pollution exposure could be an important modifiable environmental factor associated with MDD severity and which biological mechanisms mediate the negative effect of air pollution on mental health. Its results will represent an opportunity for preventive strategies, thus entailing a tremendous impact on public health.

Nanozymes Induced Air Purification- A State of the Art Review

Air decontamination is always a prime concern to society as the air to be inhaled should be free of contaminants so that living beings can get ample amount of energy to wheel their lives. Rapid urbanization causes drastic contamination in air that can create several life-threatening disorders. In this scenario, ultrahigh detection along with quick estimation of air pollutants has become an urgent need to the society. Nanotechnology enabled enzyme mimicking materials known as nanozymes, demonstrates elevated purification efficiency, sterilizability and low wind resistance. These materials when used in air-filter can work as a soldier to combat with air pollution. Incorporation of latest technology viz. Internet of Things (IoT), cloud computing, etc., brings a new flavor in air refinement. In this present work, several nanozymes are surveyed with high efficiency in air filtration. Single atom nanozymes have established themselves as proficient candidates in air decontamination. The implication in air refinement is attributed to the birth of pro-active superoxide ions and successive formaldehyde oxidation. Corona virus and microbes afflicted air may be purified by the redox commotion of nanozymes. Fiber centric air filter provides enhanced filtration efficiency in real time monitoring process with the aid of IoT conjugation. The present review aids in understanding the mechanisms and potentiality of nanozymes as air purifiers, and also optimizes the design of air purifier.

A study to access and estimation of air pollution using a multivariate statistical model in Chennai, India

Introduction: Rapid urbanization and industrial growth are the primary causes of deteriorating urban air quality in developing countries, including India. Vehicular emission is a significant cause of the degradation of air quality. It includes Air Pollution like carbon monoxide, hydrocarbons, oxides of nitrogen, oxides of sulfur, Suspended Particulate Matter (SPM), respiratory Particulate Matters (PM2.5 and PM10), and lead.
 Materials and methods: The study has considered land use,land cover, land surface temperature, vegetation, literacy rate, vehicle population, population density, and households, finding the responsible causes of air pollutionfor the analysis. Supervised classification using ArcGIS for extracting land use and land cover, Least Slack Time (LST) algorithms have used to extract land surface temperature, spatial interpolation methods like Inverse Distance Weighting (IDW) using ArcGIS for identifying the spatial distribution of Literacy rate, vehicle population, population density and households and finally the multivariate statistical model applied to identify the major responsible factor for air pollution using SPSS.
 Results: The study reveals that the particulate matter is below the standard value prescribed by the central pollution control board. The Highest air pollution is primarily responsible for vehicle population and industries. Wednesday and Thursday are the maximum pollution in Chennai, whereas it was recorded as very low on Sunday as compared to other days.
 Conclusion: Regression shows that the vehicle population is responsible for air pollution, followed by the population.

Air pollution as a substantial threat to the improvement of agricultural total factor productivity: Global evidence

ObjectiveThis study aims to provide empirical evidence about whether and to what extent air pollution affects the global agricultural total factor productivity (TFP). MethodsThe research sample covers 146 countries all over the world during 2010–2019. Two-way fixed effects panel regression models are used to estimate air pollution’s impacts. A random forest analysis is conducted to assess the relative importance of independent variables. ResultsThe results show that, on average, a 1% increase in fine particulate matter (PM2.5) and tropospheric ozone (O3) concentration would cause the agricultural TFP to decline by 0.104% and 0.207%, respectively. Air pollution’s adverse impact widely exists in various countries with different development levels, pollution degrees, and industrial structures. This study also finds that temperature has a moderating effect on the relationship between PM2.5 and agricultural TFP. PM2.5 pollution’s detrimental impact is weaker (stronger) in a warmer (cooler) climate. In addition, the random forest analysis confirms that air pollution is among the most crucial predictors of agricultural productivity. ConclusionsAir pollution is a substantial threat to the improvement of global agricultural TFP. Worldwide actions should be taken to ameliorate air quality, for the sake of agricultural sustainability and global food security.

Short-term air pollution exposure associated with death from kidney diseases: a nationwide time-stratified case-crossover study in China from 2015 to 2019

BackgroundLong-term exposure to air pollution has been associated with the onset and progression of kidney diseases, but the association between short-term exposure to air pollution and mortality of kidney diseases has not yet been reported.MethodsA nationally representative sample of 101,919 deaths from kidney diseases was collected from the Chinese Center for Disease Control and Prevention from 2015 to 2019. A time-stratified case-crossover study was applied to determine the associations. Satellite-based estimates of air pollution were assigned to each case and control day using a bilinear interpolation approach and geo-coded residential addresses. Conditional logistic regression models were constructed to estimate the associations adjusting for nonlinear splines of temperature and relative humidity.ResultsEach 10 µg/m3 increment in lag 0–1 mean concentrations of air pollutants was associated with a percent increase in death from kidney disease: 1.33% (95% confidence interval [CI]: 0.57% to 2.1%) for PM1, 0.49% (95% CI: 0.10% to 0.88%) for PM2.5, 0.32% (95% CI: 0.08% to 0.57%) for PM10, 1.26% (95% CI: 0.29% to 2.24%) for NO2, and 2.9% (95% CI: 1.68% to 4.15%) for SO2. ConclusionsOur study suggests that short-term exposure to ambient PM1, PM2.5, PM10, NO2, and SO2 might be important environmental risk factors for death due to kidney diseases in China.

Estimation of Air Pollutants and Greenhouse Gas Emission from Brick Kilns in Mymensingh

Brick kilns are becoming one of the most significant air pollution sources. It contributes to environmental and health issues in Bangladesh. This study collected data from 41 brick kilns in the Trishal Upazila utilizing a questionnaire survey and other instruments from March 12-15, 2022. In the study area, there were 80.49% Zigzag kilns, 17.07% Fixed Chimney kilns, and 2.44% Hoffman kilns. Most brick kilns had a 60-foot-tall stack, which contributed to air pollution. Brick kilns of Trishal Upazila emitted an estimated 360.628 Gg CO2, 1.104 Gg SO2, and 1.271 Gg NO2 per year where 107.143 Gg CO2, 0.328 Gg SO2, and 0.378 Gg NO2 were emitted from Balipara union and 13.066 Gg CO2, 0.04 Gg SO2, and 0.046 Gg NO2 (Based on coal) were emitted from Mathbari union. Instrument reading of CO2 ranged from 1015.58 ppm to 825 ppm, with a mean of 954.09 ppm. The average PM2.5 and PM10 values were 69 gm-3 and 116.51 gm-3, respectively. The average value of CO was 2.3 ppm. In addition, the study found that kiln employees suffered from skin diseases, respiratory issues, back and muscle pain, eye irritation, etc. Brick kilns were the primary cause of topsoil deterioration, and environmental pollution in this region. Thus, the government should adopt a new strategy to upgrade kilns' energy-efficient technology to establish brick kilns as a sustainable sector.

Evaluating reduced-form modeling tools for simulating ozone and PM2.5 monetized health impacts.

Reduced-form modeling approaches are an increasingly popular way to rapidly estimate air quality and human health impacts related to changes in air pollutant emissions. These approaches reduce computation time by making simplifying assumptions about pollutant source characteristics, transport and chemistry. Two reduced form tools used by the Environmental Protection Agency in recent assessments are source apportionment-based benefit per ton (SA BPT) and source apportionment-based air quality surfaces (SABAQS). In this work, we apply these two reduced form tools to predict changes in ambient summer-season ozone, ambient annual PM2.5 component species and monetized health benefits for multiple sector-specific emission control scenarios: on-road mobile, electricity generating units (EGUs), cement kilns, petroleum refineries, and pulp and paper facilities. We then compare results against photochemical grid and standard health model-based estimates. We additionally compare monetized PM2.5 health benefits to values derived from three reduced form tools available in the literature: the Intervention Model for Air Pollution (InMAP), Air Pollution Emission Experiments and Policy Analysis (APEEP) version 2 (AP2) and Estimating Air pollution Social Impact Using Regression (EASIUR). Ozone and PM2.5 changes derived from SABAQS for EGU scenarios were well-correlated with values obtained from photochemical modeling simulations with spatial correlation coefficients between 0.64 and 0.89 for ozone and between 0.75 and 0.94 for PM2.5. SABAQS ambient ozone and PM2.5 bias when compared to photochemical modeling predictions varied by emissions scenario: SABAQS PM2.5 changes were overpredicted by up to 46% in one scenario and underpredicted by up to 19% in another scenario; SABAQS seasonal ozone changes were overpredicted by 34% to 83%. All tools predicted total PM2.5 benefits within a factor of 2 of the full-form predictions consistent with intercomparisons of reduced form tools available in the literature. As reduced form tools evolve, it is important to continue periodic comparison with comprehensive models to identify systematic biases in estimating air pollution impacts and resulting monetized health benefits.

Impact of Construction Work Zone on Urban Traffic Environment

Abstract Objectives Traffic management is challenging during construction because of the effects of traffic congestion, travel time, delay, and queue length. Long-term work zones on urban roads lead to many problems such as speed, inconvenience, and economic losses to drivers, which are focused on in previous studies. Methods Moreover, due to the construction work zone (CWZ), the impact on environmental factors such as air quality and noise levels was not focused on. Because of the building work zones, this research focused on comprehending how traffic congestion measurements and environmental factors affect urban traffic management. Findings The present research uses TransCAD to estimate air pollution due to increased traffic in the urban areas. Furthermore, three nonlinear AI-based models (ANFIS, FFNN, and SVR) and one linear black box model were developed to predict the noise level in the city, in which each contained the total traffic and speed as well as the ratio of heavy vehicles in the traffic. Novelty For traffic control, a variety of techniques are available, including video data analysis, infrared sensors, inductive loop detection, wireless sensor networks, etc. These are all practical techniques for efficient traffic management. It is necessary to conduct studies on the amount of traffic, the topography, accidents, time delays, and the level of safety offered in the work area. Construction operations are facilitated by the implementation of traffic flow, and during this process, long-term CWZs are inevitable. Therefore, the proposed model accomplishes the goal, namely that only analytical research and a few traffic diverter signs point drivers to alternate routes to their destinations.

Association of short-term exposure to ambient fine particle matter with hospital admission risks and costs in China, a case-crossover study

As Fine particulate matter (PM2.5) levels declined in China in past years, the health and social benefits brought about by the air quality improvement are beginning to emerge. We did a time-stratified, case-crossover study which including in total of 106, 120, 862 hospital admissions from 1040 class 3 hospitals in 268 cities, 31 provinces in China, from January 1, 2013 to December 31, 2017, obtained from High Quality Monitoring System (HQMS). We obtained PM2.5 and ozone concentrations from a high-resolution model and joined daily air pollution estimates for each patient based on hospital location. To assess the association between main air pollutants and hospital admission, hospital days, and hospitalization expenses, we applied conditional logistic regressions to perform a risk assessment associated with main pollutants (PM2.5 and Ozone) in two-pollutant models. The results show that the risk of hospital admission was positively associated with short-term exposure to PM2.5 in all major diseases. Specifically, every 10 μg/m3 increase in the concentration level of PM2.5 (adjusted for ozone) was associated with 0.27% (95% CI: 0.25%, 0.29%) increase in hospital admission, 39.00 (95% CI: 36.11, 41.89) yuan increase in hospitalization expense per admission, and 0.0280 (95% CI: 0.0259, 0.0300) days increase in hospital days per admission. Moreover, the relative risk increases of mental disorders, respiratory diseases and circulatory diseases showed significant associations with short-term PM2.5 exposure. Based on our calculations, improved air quality since 2013 saved 2.28 billion yuan in medical expenses in 2017.

Are air quality perception and PM2.5 exposure differently associated with cardiovascular and respiratory disease mortality in Brussels? Findings from a census-based study

BackgroundThere is ample evidence that air pollution increases mortality risk, but most studies are based on modelled estimates of air pollution, while the subjective perception of air quality is scarcely assessed. We aimed to compare the effects of objective and subjective exposure to air pollution on cardiorespiratory mortality in Brussels, Belgium. MethodsData consisted of the 2001 Belgian census linked to registry-based mortality data for the follow-up period 2001–2014. We included individuals aged >30 years of age residing in Brussels at baseline (2001). Air pollution exposure was assessed with objective (modelled annual mean concentrations of PM2.5 in micrograms per cubic metre, μg/m3) and subjective indicators (poor self-reported air quality perception in the census). We used Cox Proportional Hazard models with age as the underlying time scale to evaluate associations with cardiovascular disease (CVD) and respiratory disease mortality, and separately, ischaemic heart disease (IHD), cerebrovascular disease, and COPD excluding asthma mortality. We specified single- and two-exposure models and evaluated effect modification by neighbourhood unemployment rate. Results437,340 individuals were included at baseline. During follow-up (2001–2014), 22,821 (5%) individuals had died from CVDs and 8572 (2%) from respiratory diseases. In single-exposure models, PM2.5 was significantly associated with an increased risk in CVD and IHD mortality (e.g. for IHD, per 5 μg/m3 increase: Hazard Ratio, HR:1.22, 95%CI:1.08–1.37), and poor air quality perception with COPD excluding asthma mortality (HR:1.23, 95%CI:1.15–1.33). Associations remained significant in the two-exposure models, and additionally, perception was associated with respiratory disease mortality. Associations became gradually stronger with increasing neighbourhood unemployment rate [e.g. in the highest, Q3: PM2.5 and cerebrovascular disease mortality (HR:1.53, 95%CI:1.04–2.24)]. ConclusionOur findings suggest that objective and subjective exposure to air pollution increased the risk of dying from cardiovascular and respiratory diseases respectively in Brussels. These results encourage policies reducing pollution load in Brussels whilst considering socio-economic inequalities.

Ship Air Pollution Estimation by AIS Data: Case Port of Klaipeda

Ships operating on fossil fuel release pollutant emissions into the atmosphere. Released pollutants have a negative effect on the environment and human health, especially in port cities. For this reason, it is very important to properly evaluate these emissions so they can be managed. The current and most common methodologies for shipping pollution evaluation are used for whole port areas or larger terminals over a long period of time and are not analyzed in terms of detailed activity, which may lead to underestimations in certain areas. This study aims to evaluate emissions from ships in port by combining ships’ technical, AIS and EMEP data that allow us to evaluate emissions in port, not as a singular area source but enables individual ship emissions evaluation at any given point in time. To achieve this emission calculation, an algorithm was compiled by using EMEP/EEA Tier 3 methodology. The developed method presents a way to evaluate emissions in a detailed manner not only for groups of ships but also for individual ships if that is required. This method also lets us analyze shipping emissions’ intensity throughout all port territory and identify the most excessive pollution sources. However, the method adds additional work for researchers because of the huge data arrays required for complex calculations.

Developing Machine learning models for hyperlocal traffic related particulate matter concentration mapping

Recently, estimating air pollution concentrations and contributions from various sources has become a major research focus. Our research adds to the body of knowledge by developing machine learning (ML) models to avoid intermediate modeling steps using traffic and meteorological data. The ML model's overall performance in predicting air pollution concentrations at receptors outperforms previous methods. Our best model, Convolutional Long Short-Term Memory (ConvLSTM), has a Mean Relative Error (MRE) of 38.9%, which is lower than the 47.5% MRE for the single hidden layer model, the 63.2% MRE for the Convolutional Neural Network model, and the 41.5% MRE for ConvLSTM with time-series data. Memory cells help the ConvLSTM model predict a large number of spatially correlated observations. The novel ML modeling approach has low data requirements and is computationally efficient, which makes it promising for future transportation planning, epidemiology, and environmental justice assessments.

Associations between greenspace surrounding schools and lipid levels in Chinese children and teenagers

Little evidence exists concerning the associations of greenspace with childhood lipid profiles and dyslipidemias, especially in developing countries and regions. We aimed to investigate the associations of greenspace surrounding schools with lipid levels and dyslipidemia prevalence among Chinese children and teenagers. We obtained baseline information and health data of 10,408 children and teenagers (aged 6–18 years) who studied from 94 schools in China. We measured levels of four blood lipids: triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C). Dyslipidemias were defined using standard recommendations. Greenness surrounding schools were assessed using two satellite-based greenness indices, Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) at 300-, 500-, and 1000-m circular buffers based on each school's latitude and longitude. We used random forest model combined with meteorological and remote sensing data to estimate air pollution levels surrounding each school. We used generalized linear mixed models to estimate the associations of greenness with lipid levels and dyslipidemias prevalence. We also performed sub-group and mediation analyses. An interquartile range (IQR) increase in NDVI500m was significantly associated with a 0.064 mmol/L (95% confidence interval [CI]: 0.083, −0.045) and 0.049 mmol/L (95% CI: 0.065, −0.033) decreased TC and LDL-C levels, respectively, as well as a 0.13-fold (95% CI: 0.01, 0.23) and 0.17-fold (95% CI: 0.01, 0.30) decreased odds of hypercholesterolemia and hyperbetalipoproteinemia, respectively. Associations were stronger in students aged ≤12 years and born to parents having lower education levels compared to their counterparts. Particle with aerodynamic diameter ≤2.5 μm (PM2.5) mediated 61.5% and 16.7% of the association of greenness with TG and LDL-C levels, respectively. In summary, higher school-based greenness exposure was beneficially associated with lipid levels among Chinese children and adolescents, and part of the association can be explained by lowed PM2.5 levels.

Lichen Biodiversity Index (LBI) for the Assessment of Air Quality in an Industrial City in Pahang, Malaysia

Lichen is a well-documented useful biological indicator for monitoring air pollution using various methods such as the biodiversity of lichens. However, the use of lichen biodiversity to estimate air pollution levels in industrial areas, particularly in Malaysia, is still weak, and the determinants of its diversity are unknown. As a result, the purpose of this research is to analyse air pollution in Malaysia’s industrial urban area using the lichen biodiversity index and its determining factor. This research was carried out at Gebeng, Pahang, Malaysia. A total of 14 sample locations were chosen, each with three replication stations. The Lichen Biodiversity Index (LBI) approach was employed in this study to estimate the degree of air pollution in Gebeng. This study also investigated three potential determinants of lichen biodiversity: carbon monoxide (CO) concentration, relative humidity (%), and vehicle motor frequency (per hour). The LBI was plotted and analysed using the Geographical Information System (GIS) programme ArcGIS 10.8.1, and the determining variables were identified using Pearson’s Correlation Coefficient software PAST 4.03. This study discovered a total of 11 lichen species known as metropolitan lichen. The average LBI across Gebeng is 19.5 (moderate alteration). In the industrial region, CO has an inversely significant relation with lichen biodiversity (r = −0.7433), relative humidity has a significant relation with lichen biodiversity (r = 0.8249), and vehicle motors are not significant as a determining factor for lichen biodiversity (r = 0.2068). This study demonstrates that lichen, with its diversity of species in one place, can be utilised to measure and quantify the degree of air pollution in industrial areas. In addition to that, in the context of an industrial city, vehicle motors do not have a significant impact on lichen biodiversity due to the existence of other pollutants sources from industrial activities. Relative humidity is a sign of clean air and humid surroundings, which is good for lichen growth; meanwhile, higher CO concentration will restrict the growth of sensitive-type lichen and will only allow the resistant type of lichen to grow.

Air pollutant emissions from global food systems are responsible for environmental impacts, crop losses and mortality.

Food systems are important contributors to global emissions of air pollutants. Here, building on the EDGAR-FOOD database of greenhouse gas emissions, we estimate major air pollutant compounds emitted by different stages of the food system, at country level, during the past 50 years, resulting from food production, processing, packaging, transport, retail, consumption and disposal. Air pollutant estimates from food systems include total nitrogen and its components (N2O, NH3 and NOx), SO2, CO, non-methane volatile organic compounds (NMVOC) and particulate matter (PM10, PM2.5, black carbon and organic carbon). We show that 10% to 90% of air pollutant emissions come from food systems, resulting from steady increases over the past five decades. In 2018, more than half of total N (and 87% of ammonia) emissions come from food systems and up to 35% of particulate matter. Food system emissions are responsible for about 22.4% of global mortality due to poor air quality and 1.4% of global crop production losses.

Estimation of Air Pollution from Ships in Port Area: A Case Study of Yeosu and Gwangyang Ports in Korea

With the rapid development of global trade and maritime transportation, a comprehensive emission inventory is necessary to control air pollution from ships as well as to comply with international environmental standards. The purpose of this study is to calculate the volume of emissions from ships in Yeosu and Gwangyang ports in Korea based on a bottom-up approach and supported by the data from the automatic identification system (AIS) and Korean port management information system (Port-MIS). Specifically, a real set of data on specifications of all ships operating at the port in 2019 was collected and an analysis of ship movement using AIS data was conducted by the authors in this study to divide the route into cruising and maneuvering phases as well as to estimate engine’s load factor. Finally, results show that the total amount of air emissions from ships at the port area was 558 thousand tons, which is 96% from CO2 and 66% from hoteling mode. Additionally, container and tanker ships together emitted the highest volume of air pollution accounting for a share of 73%.

Locally weighted total least-squares variance component estimation for modeling urban air pollution.

Land use regression (LUR) models are one of the standard methods for estimating air pollution concentration in urban areas. These models are usually low accurate due to inappropriate stochastic models (weight matrix). Furthermore, the measurement or modeling of dependent and independent variables used in LUR models is affected by various errors, which indicates the need to use an efficient stochastic and functional model to achieve the best estimation. This study proposes a locally weighted total least-squares variance component estimation (LW-TLS-VCE) for modeling urban air pollution. In the proposed method, in the first step, a locally weighted total least-squares (LW-TLS) regression is developed to simultaneously considers the non-stationary effects and errors of dependent and independent variables. In the second step, the variance components of the stochastic model are estimated to achieve the best linear unbiased estimation of unknowns. The efficiency of the proposed method is evaluated by modeling PM2.5 concentrations via meteorological, land use, and traffic variables in Isfahan, Iran. The benefits provided by the proposed method, including considering non-stationary effects and random errors of all variables, besides estimating the actual variance of observations, are evaluated by comparing four consecutive methods. The obtained results demonstrate that using a suitable stochastic and functional model will significantly increase the proposed method's efficiency in PM2.5 modeling.

The burden of premature mortality from coal-fired power plants in India is high and inequitable

Prior mortality estimates of air pollution from coal-fired power plants in India use PM2.5 exposure-response functions from settings that may not be representative, and do not include other potentially harmful effects of these plants, such as fly ash pollution and heavy freshwater consumption. We use a national, district level dataset to assess the impact of coal-fired power plants on all-cause mortality (15–69 years) in 2014. We compare districts with coal-fired power plants (total capacity >1000 MW) to districts without a coal-fired power plant, estimating the effect of these power plants on all-cause mortality within districts that have these plants. Out of 597 districts in India in 2014, 60 districts had a coal-fired power plant. When compared to districts without a coal-fired power plant, districts with a coal-fired power plant (>1000 MW) had higher rates of age-standardized mortality in both women (0.38, 95% CI: −0.14–0.90) and men (0.55, −0.17–1.27). Similarly, these districts had higher rates of conditional probability of premature death in both women (2.22, −0.13–4.56) and men (2.12, −0.54–4.77). The point estimates for total excess deaths were 19 320 for women and 27 727 for men. In affected districts, the proportion of premature adult deaths attributable to coal-fired power plants was 5.8% (−0.3%–11.9%) in women and 4.3% (−1.1%–9.6%) in men. We estimate that ∼47 000 premature adult deaths can be attributed to large coal-fired power plants in India in 2014. These deaths are concentrated in the ∼10% of districts that have the nation’s power plants, where they are associated with 1 out of 20 premature adult deaths. Effective regulation of emissions from these plants, coupled with a phaseout of coal-fired power plants, can help decrease this burden of inequitable and premature adult mortality.