Industrial Air Pollutant Emissions Research Articles

Emission Rate Estimation of Industrial Air Pollutant Emissions Based on Mobile Observation

Mobile observation has been widely used in the monitoring of air pollution. However, studies on pollution sources and emission characteristics based on mobile navigational observation are rarely reported in the literature. A method for quantitative source analysis for industrial air pollutant emissions based on mobile observations is introduced in this paper. NOx pollution identified in mobile observations is used as an example of the development of the method. A dispersion modeling scheme that fine-tuned the meteorological parameters according to the actual meteorological conditions was adopted to minimize the impact of uncertainties in meteorological conditions on the accuracy of small-scale dispersion modeling. The matching degree between simulated and observed concentrations was effectively improved through this optimization search. In response to the efficiency requirements of source resolution for multiple sources, a random search algorithm was first used to generate candidate solution samples, and then the solution samples were evaluated and optimized. Meanwhile, the new index Smatch was established to evaluate the quality of candidate samples, considering both numerical error and spatial distribution error of concentration, in order to address the non-uniqueness of the solution in the multi-source problem. Then, the necessity of considering the spatial distribution error of concentration is analyzed with the case study. The average values of NOx emission rates for the two study cases were calculated as 69.8 g/s and 70.8 g/s. The Smatch scores were 0.92–0.97 and 0.92–0.99. The results were close to the online monitoring data, and this kind of pollutant emission monitoring based on the mobile observation experiment was initially considered feasible. Additional analysis and clarifications were provided in the discussion section on the impact of uncertainties in meteorological conditions, the establishment of a priori emission inventories, and the interpretation of inverse calculation results.

Model of Analysis of Ecological-Economic Processes

This article presents a model for assessing the impact of environmental factors on economic performance and optimizing industrial production taking into account environmental costs. The modeling showed the influence of production and environmental parameters on the sustainable development of the economy and revealed the dynamic dependence of the volume of industrial production on time. The model can be used to analyze and predict the effectiveness of environmental measures, as well as to make smart decisions in the field of sustainable development and environmental protection. The developed program shows how the volumes of industrial production and pollutants emitted into the atmosphere change in various areas. Model parameters include initial values of industrial production, volumes of labor resources, capital utilization rates, cleanup costs, depreciation, disposal of funds, minimum values of production and environmental funds, and coefficients of the impact of pollution on the economy. The simulation calculates production volumes, pollution treatment, environmental damage, and environmental income, and then updates industrial production volumes considering depreciation, accumulation, and diffusion. The results demonstrate the dynamics of changes in the production of industrial products and make it possible to determine the volumes of industrial production and pollutants emitted into the atmosphere by region. Graphical results presentations illustrate temporal changes in industrial production and air pollutant emissions by region, facilitating the understanding and management of environmental and economic interactions.

Controlling Industrial Air-Pollutant Emissions under Multi-Factor Interactions Based on a Developed Hybrid-Factorial Environmental Input–Output Model

A hybrid-factorial environmental input–output model (HEIM) is proposed for controlling industrial energy-related air pollution. HEIM has the advantages of analyzing industrial air-pollutant emission system (IAES) performance, quantifying key factors’ individual and reciprocal effects on the system, generating optimal system planning strategies under multiple scenarios. HEIM is then applied to Fujian province, which is a special economic development region in China. The significant findings are as follows: (i) the main sectors of pollutants’ (NOx, SO2, PM and VOCs) emissions are electricity supply (ELE), transportation (TRA), nonmetal minerals (NON), chemical products (CHE) and metal processing (MET); (ii) the proportion of air pollutants (NOx, SO2 and PM) emitted from energy activities can reach 83.8%, 88.6% and 68.1% of the province’s total emissions, implying that it is desired for industrial activities to improve the energy efficiency and promote cleaner production; (iii) the system robustness was between 0.287 and 0.321 (maximum value is 0.368), indicating the emission structure of IAES was not healthy; (iv) the contributions of the key factors to air-pollutant emission equivalent are NOx emission (51.6%) > ELE coal consumption (25.8%) > SO2 emission (12.5%); (v) the contributions of the key factors affecting system robustness are equipment manufacturing’s (EQU) direct consumption coefficient (81.4%) > CHE coal consumption (11.7%) > NON coal consumption (5.0%). The optimal strategies should strictly control ELE coal consumption (replaced by clean energy) and strictly limit NOx and SO2 emissions (e.g., technology upgrade) from the main sectors.

Systemic autoimmune rheumatic diseases and multiple industrial air pollutant emissions: A large general population Canadian cohort analysis

BackgroundPast investigations of air pollution and systemic autoimmune rheumatic diseases (SARDs) typically focused on individual (not mixed) and overall environmental emissions. We assessed mixtures of industrial emissions of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and sulfur dioxide (SO2) and SARDs onset in Ontario, Canada. MethodsWe assembled an open cohort of over 12 million adults (without SARD diagnoses at cohort entry) based on provincial health data for 2007–2020 and followed them until SARD onset, death, emigration, or end of study (December 2020). SARDs were identified using physician billing and hospitalization diagnostic codes for systemic lupus, scleroderma, myositis, undifferentiated connective tissue disease, and Sjogren’s. Rheumatoid arthritis and vasculitis were not included. Average PM2.5, NO2, and SO2 industrial emissions from 2002 to one year before SARDs onset or end of study were assigned using residential postal codes. A quantile g-computation model for time to SARD onset was developed for the industrial emission mixture, adjusting for sex, age, income, rurality index, chronic obstructive pulmonary disease (as a proxy for smoking), background (environmental overall) PM2.5, and calendar year. We conducted stratified analyses across age, sex, and rurality. ResultsWe identified 43,931 new SARD diagnoses across 143,799,564 person-years. The adjusted hazard ratio for SARD onset for an increase in all emissions by one decile was 1.018 (95% confidence interval 1.013–1.022). Similar positive associations between SARDs and the mixed emissions were observed in most stratified analyses. Industrial PM2.5 contributed most to SARD risk. ConclusionsIndustrial air pollution emissions were associated with SARDs risk.

Chinese industrial air pollution emissions based on the continuous emission monitoring systems network

As the world’s largest industrial producer, China has generated large amount of industrial atmospheric pollution, particularly for particulate matter (PM), SO2 and NOx emissions. A nationwide, time-varying, and up-to-date air pollutant emission inventory by industrial sources has great significance to understanding industrial emission characteristics. Here, we present a nationwide database of industrial emissions named Chinese Industrial Emissions Database (CIED), using the real smokestack concentrations from China’s continuous emission monitoring systems (CEMS) network during 2015–2018 to enhance the estimation accuracy. This hourly, source-level CEMS data enables us to directly estimate industrial emission factors and absolute emissions, avoiding the use of many assumptions and indirect parameters that are common in existing research. The uncertainty analysis of CIED database shows that the uncertainty ranges are quite small, within ±7.2% for emission factors and ±4.0% for emissions, indicating the reliability of our estimates. This dataset provides specific information on smokestack concentrations, emissions factors, activity data and absolute emissions for China’s industrial emission sources, which can offer insights into associated scientific studies and future policymaking.

Industrial air pollutant emissions and mortality from Alzheimer's disease in Canada

BackgroundThere is increasing interest in the health effects of source-specific air pollution. However, the relationship between industrial air pollutants and Alzheimer's disease has received limited investigation. ObjectivesTo assess associations of industrial fine particulate matter (PM2.5), nitrogen dioxide (NO2), and sulfur dioxide (SO2) exposures with mortality from Alzheimer's disease. MethodsApproximately 3.2 million adults involved in the 2006 Canadian Census Health and Environment Cohort (CanCHEC) were followed from Census day (May 16, 2006) until death or December 31, 2016. Three-year moving-average industrial emissions with a one-year lag were assigned to the participants based on their residential postal codes. The neighborhood emission of each of the three industrial air pollutants for a postal code was estimated by considering weights of the air pollutant emissions from all industries within a 15 km buffer area, distances between the postal code area and the emitters, and percentages of time per year that the postal code area was downwind of the industrial emitters. Cox proportional hazards models were used to compute hazard ratios (HRs) for deaths from Alzheimer's, adjusting for 15 socio-demographic and contextual covariates. Sensitivity analyses were conducted by adjusting for other industrial emissions, greenness, and comorbidity index, individually. ResultsWe identified 4500 deaths due to Alzheimer's disease from 2006 to 2016 for a total of 32,909,200 person-years across the follow-up period. The adjusted HR for mortality from Alzheimer's related to one interquartile range increase in industrial PM2.5, NO2, and SO2 tonnes/meter per year are 1.006 (95% confidence intervals: 1.000-1.011), 0.994 (0.978-1.011), and 0.998 (0.996-1.001), respectively. Similar positive associations between industrial PM2.5 and mortality from Alzheimer's disease were observed, but there were no clear associations for NO2 and SO2 in sensitivity analyses. ConclusionsExposure to industrial PM2.5 increases the risk of mortality from Alzheimer's disease.

Industrial Air Pollutant Emissions and Mortality from Alzheimer's Disease in Canada

Industrial Air Pollutant Emissions and Mortality from Alzheimer's Disease in Canada

The effects of emission trading scheme on industrial output and air pollution emissions under city heterogeneity in China

Ecological degradation has become one of the constraints on the development of human society. To deal with pollutant emissions, governments around the world have implemented emission trading schemes (ETS). However, although the effectiveness of ETS policies has been widely acknowledged, it is unclear whether the policy effects depend on regional characteristics. To address this research gap, this study integrated the propensity score matching method and multi-period difference-in-difference model (PSM-DID) to examine the impacts of ETS on industrial output and pollution emissions, and the influence of city heterogeneity on policy effects. The results showed that the ETS positively affected industrial output and negatively affected pollutant emissions in the pilot cities. Furthermore, implementation of the ETS was more conducive to increasing industrial output while reducing emissions in cities with larger populations, higher financial development levels, and worse air quality. Nevertheless, in lower industrialized cities, the ETS implementation was more prominent in promoting industrial output. In this case, the environmental effect was lower than that in cities with higher industrialized. The findings provide a better understanding of city heterogeneity of ETS policy effects.

The Environmental Effects of International Trade in China: Measuring the Mediating Effects of Technology Spillovers of Import Trade on Industrial Air Pollution

China is in a strategic phase of an industrial green transformation. Industrial air pollution is a key environmental target for governance. Because import trade is a core channel through which advanced environmental protection technology is absorbed, the question of whether technology spillovers brought about by import trade can reduce industrial air pollution emissions is a topic worth exploring. This paper uses a generalized spatial two-stage least-square (GS2SLS) model to explore the impact of import trade technology spillovers on industrial air pollution emission intensities using panel data from 30 provinces and cities between 2000 and 2017. Economic scale, industrial structure, and technological innovation are used as intermediary variables to test whether they play mediating effects. The results show that: (1) capital and intermediate goods technology spillovers directly reduce industrial air pollution emission intensity and (2) import trade technology spillovers indirectly reduce emission intensities by expanding economic scale, optimizing industrial structure, and enhancing technological innovation through mediating variables. Furthermore, industrial structure optimization and technological innovation have the largest mediating effects on industrial SO2, while economic expansion has the most significant mediating effect on industrial smoke and dust. The mediating effects of technology spillovers from intermediate goods exceed those of capital technology spillovers. Finally, industrial air pollution emission intensity demonstrates both spatial agglomeration and time lag effects. Environmental regulations and energy structure are shown to increase industrial air pollution emissions, while urbanization and foreign direct investment reduce industrial air pollution. Based upon these research results, some pertinent policy implications are proposed for China.

Collaborative Governance Mechanism of Climate Change and Air Pollution: Evidence from China

Since entering the industrialized era, China’s greenhouse gas emissions and air pollutant emissions have increased rapidly. China is the country with the most greenhouse gas emissions, and it is also facing serious local air pollution problems. China’s industrial sector is the largest contributor to CO2 and air pollutants. The resulting climate change and air pollution issues have caused China to face double pressures. This article uses the CO2 and comprehensive air pollutant emission data of China’s industrial sector as a starting point and uses econometric research methods to explore the synergy between China’s industrial carbon emission reduction and industrial comprehensive air pollutant emission reduction. The synergistic effect between industrial carbon emissions and industrial comprehensive air pollutant emissions has been quantified, and the transmission path of the synergistic effect has been explored. The empirical results show that there are benefits of synergistic governance between climate change and air pollution in China’s industrial sector. Every 1000 tons of carbon reduction in the industrial sector will result in 1 ton of comprehensive air pollutant reduction. The increase in R&D expenditure in the energy and power sector can significantly promote the reduction of air pollutants in the industrial sector. Increasing the intensity of environmental regulations is the main expansion path for synergy. However, in eastern, central, and western China, the synergy is not the same. Therefore, it is necessary to formulate regionally differentiated emission reduction policies. The research conclusions of this article can provide policy references for the coordinated governance of climate change and air pollution in China.

Understanding the industrial NOx and SO2 pollutant emissions in China from sector linkage perspective

Since the most stringent-ever clean air policy was implemented in 2013 in China, main industrial air pollutant emissions have notably decreased. However, there are few studies on air pollutant emissions of industrial sectors driven by supply chain before and after implementing this policy. This paper attempts to provide a new perspective from industrial linkage to understand the emission of air pollutants. Based on Input-Output model framework, we revealed the linkages of SO2 and NOx emissions between sectors from 2012 to 2017 and the driving forces behind emission changes. Moreover, we simulated the possible impact of the key sector linkages on air pollutant emissions. Results show that the most noteworthy change during this period is that the metal melting sector has replaced the power sector, as the largest pollutant output emission sector associated with other sectors, especially the transport equipment sector. The main reason of this phenomena is that the emission intensity reduction rate of metal smelting sector (e.g., only 17% for NOx) is far less than other sectors. In the future, the development of the equipment manufacturing may put pressure on the metal smelting sector to reduce emissions. For example, when the transport equipment sector increases total output by 20% ~ 40%, the metal smelting sector will be driven to emit 0.04Mt ~0.08Mt of NOx. This paper provides a basis to quantitatively analyze the industrial sector linkages and identify the key sectors from 2012–2017, and helps decision makers better understand the impact of sector linkage on pollutant emissions.

Improving energy use and mitigating pollutant emissions across “Three Regions and Ten Urban Agglomerations”: A city-level productivity growth decomposition

Analyzing green transformation of energy use and pollutant emissions in China’s “Three Regions and Ten Urban Agglomerations” (TRTAs) allows effectively promoting sustainable development in the country. This paper applies Data Envelopment Analysis, namely the Bounded-adjusted Measure (BAM) relying on the additive structure, to measure the technical inefficiency and productivity change across TRTAs in China. The technical inefficiency and productivity change observed for TRTAs are 0.29 and 2.29% respectively. The decomposition results indicate that industrial energy consumption, industrial sulfur dioxide (SO2), and industrial soot & dust emissions are the main variables causing TRTAs’ industrial operation inefficiency. Spatially, environmental inefficiency of the above-mentioned three variables in North China Urban Agglomeration (NA) is higher than those in Northwest, Yangtze and Pearl River Delta Urban Agglomerations (NWA, YRDA, and PRDA respectively). Decomposition of the Gini coefficient shows that the performance associated with the three variables varies among the regions with intra-regional differentiation of the PRDA being the highest. Given the regional patterns in productivity change, more efforts should be directed towards improving technical progress on industrial energy conservation in NWA. Furthermore, regulations on industrial air pollutant emissions, joint mitigation and monitoring of the key indicators in PRDA should also be emphasized.

The impact of COVID-19 control measures on air quality in China

The outbreak of Coronavirus Disease 2019 (COVID-19) in China in January 2020 prompted substantial control measures including social distancing measures, suspension of public transport and industry, and widespread cordon sanitaires (‘lockdowns’), that have led to a decrease in industrial activity and air pollution emissions over a prolonged period. We use a 5 year dataset from China’s air quality monitoring network to assess the impact of control measures on air pollution. Pollutant concentration time series are decomposed to account for the inter-annual trend, seasonal cycles and the effect of Lunar New Year, which coincided with the COVID-19 outbreak. Over 2015–2019, there were significant negative trends in particulate matter (PM2.5, −6% yr−1) and sulphur dioxide (SO2, −12% yr−1) and nitrogen dioxide (NO2, −2.2% yr−1) whereas there were positive trends in ozone (O3, + 2.8% yr−1). We quantify the change in air quality during the LNY holiday week, during which pollutant concentrations increase on LNY’s day, followed by reduced concentrations in the rest of the week. After accounting for interannual trends and LNY we find NO2 and PM concentrations were significantly lower during the lockdown period than would be expected, but there were no significant impacts on O3. Largest reductions occurred in NO2, with concentrations 27.0% lower on average across China, during the lockdown. Average concentrations of PM2.5 and PM10 across China were respectively 10.5% and 21.4% lower during the lockdown period. The largest reductions were in Hubei province, where NO2 concentrations were 50.5% lower than expected during the lockdown. Concentrations of affected pollutants returned to expected levels during April, after control measures were relaxed.

Socioeconomic factors of industrial air pollutants in Zhejiang Province, China: Decoupling and Decomposition analysis.

In order to analyze and control air pollutant emissions effectively, on the basis of comprehensive consideration of three different pollution sources of industrial sulfur dioxide, industrial nitrogen oxides, and industrial smoke and dust, the Tapio decoupling model and LMDI decomposition model with six decomposition variables are constructed to compare the effects of socioeconomic factors on industrial air pollutant emissions in 11 cities in Zhejiang Province during 2006-2017. Then, a decoupling effort model is developed to analyze the effectiveness of the decoupling efforts taken at city level. This study found that (1) during the period of 2006-2017, the air pollutant emission reduction work in Zhejiang Province achieved remarkable results. More specifically, economic scale effect and population effect are the main factors for the increase of air pollutant emissions. And, the energy emission intensity effect and technological progress are the main driving forces for the reduction of three atmospheric pollutants, followed by the reduction effect of industrial structure and energy structure. (2) The environmental pollution problems of different air pollution sources in different cities are heterogeneous. (3) Eleven cities in Zhejiang Province have made significant decoupling efforts on the emission of three kinds of air pollutants, but there are some differences in the trend of the decoupling effort index of different pollution sources in different cities. In the future, illustrating by the example of Zhejiang, we should implement a "common but different" emission reduction strategy and emphasize pollutant emissions control during energy use in the efforts of further promoting the reduction of air pollutants.

Variation of industrial air pollution emissions based on VIIRS thermal anomaly data

The Beijing-Tianjin-Hebei (BTH) region is the largest industrial cluster in northern China and its most prominent air pollution zone. Industrial emissions of PM2.5, SO2, NOx and other pollutants have a significant impact on regional air quality. Monitoring industrial pollution emissions is of great significance for the prevention and control of air pollution. The VIIRS Nightfire products can detect thermal anomalies in heavy industrial sectors, thus providing a new means for monitoring industrial activities. In this study, the VIIRS 375 product was selected to study the spatial and temporal changes of the heavy industrial thermal anomalies through spatial analysis and mathematical statistics methods. Furthermore, it is the first time to use thermal anomaly radiative power (CTRP) to evaluate the spatiotemporal pattern of industrial pollution emissions and its impact on air quality in the BTH region. We observed that the spatial distribution of CTRP was unbalanced and that Tangshan-Tianjin and Handan-Xingtai- Shijiazhuang were found to be high-density pollution areas. The CTRP in Tangshan and Handan were higher than in other cities, accounting for 72.41% of their area. From 2012 to 2018, CTRP in this region decreased by 17.2% per year while in Langfang decreased by 72.9% per year. The CTRP also showed a significantly positive correlation with industrial energy consumption. Emissions of SO2, NO2, and PM2.5 were high in the area of intense distribution of thermal anomalies. When compared to the density of tropospheric NO2 and SO2 columns and the monitored concentrations of SO2 and PM2.5, CTRP was consistent with the trend of interannual variations and spatial distributions. The energy-saving and emissions reduction measures taken by the industrial sector in the BTH contributed significantly to improving regional air quality. Remote sensing-based monitoring can provide regional industrial energy consumption in real-time and reflect pollution emissions, providing a basis for industrial pollution emissions regulation and reduction policy.

Decomposition and measures of the driving factors for China’s industrial air pollution emissions at the prefectural level

The decomposition and measures of industrial air pollution emissions’ driving forces in China are helpful to sustainable development. They are also instructive for the Chinese government to functionally formulate policies for energy conservation and emission reduction, as well as to deal with climate change in practice. This study, for the first time, has applied Kaya identity and LMDI index decomposition method to empirically decompose and measure the driving factors of China’s industrial air pollution emissions based on prefecture-level cities. The results suggest that, over the past ten years, the per capita industrial air pollution emissions have increased from both state and regional perspective. The main reason for the increasing trend is the economy’s scale effects. Meanwhile, the technological effects and structural effects reduce pollution emissions in the long term. However the environmental-beneficial technology and structural effects are smaller than the scale effects, therefore such an increasing trend cannot be offset by the technological effects.

Shadow prices of industrial air pollutant emissions in China

Shadow prices of undesirable outputs assess the opportunity costs of emissions reduction and provide a comprehensive measuring indicator for the overall environmental regulation stringency. Shadow prices of industrial air pollutant emissions in China for 2006–2016 have been estimated by different nonparametric models, and their determinants, as well as the emissions controlling effects of environmental regulations, have been further investigated in this study. Our empirical results reveal that the conventional polluting technology model is consistent with the two sub-technologies model, and that shadow prices estimated by different models differed in absolute values but shared the same growth trends and regional characteristics. Decomposition results according to the logarithmic mean divisia index (LMDI) method indicate that variations in the shadow prices were due to the combined forces of the energy saving and end-of-pipe controlling effects in the analyzed periods. Moreover, we found that China’s environmental regulations with regional disparities in recent years (2006–2016) have effectively reduced industrial air pollutant emissions at an increasing rate, and that the environmental taxes imposed in 2018 still followed the existing regional unbalances of environmental regulation stringency.

Exposure to industrial air pollutant emissions and asthma-related effects in children

Exposure to industrial air pollutant emissions and asthma-related effects in children

Determining broad scale associations between air pollutants and urban forestry: A novel multifaceted methodological approach

Global urbanisation has resulted in population densification, which is associated with increased air pollution, mainly from anthropogenic sources. One of the systems proposed to mitigate urban air pollution is urban forestry. This study quantified the spatial associations between concentrations of CO, NO₂, SO₂, and PM₁₀ and urban forestry, whilst correcting for anthropogenic sources and sinks, thus explicitly testing the hypothesis that urban forestry is spatially associated with reduced air pollution on a city scale. A Land Use Regression (LUR) model was constructed by combining air pollutant concentrations with environmental variables, such as land cover type and use, to develop predictive models for air pollutant concentrations. Traffic density and industrial air pollutant emissions were added to the model as covariables to permit testing of the main effects after correcting for these air pollutant sources. It was found that the concentrations of all air pollutants were negatively correlated with tree canopy cover and positively correlated with dwelling density, population density and traffic count. The LUR models enabled the establishment of a statistically significant spatial relationship between urban forestry and air pollution mitigation. These findings further demonstrate the spatial relationships between urban forestry and reduced air pollution on a city-wide scale, and could be of value in developing planning policies focused on urban greening.

A Population-Based Birth Cohort Study of the Association between Childhood-Onset Asthma and Exposure to Industrial Air Pollutant Emissions

Background: Studies of the association between air pollution and asthma onset have mostly focused on urban and traffic-related air pollution. We investigated the associations between exposure to in...