Reduce Air Pollutant Emissions Research Articles

Improving the Air Quality Management: The Air Pollutant and Carbon Emission and Air Quality Model for Air Pollutant and Carbon Emission Reduction in the Iron and Steel Industries of Tangshan, Hebei Province, China

Currently, Tangshan confronts the dual challenge of elevated carbon emissions and substantial pollution discharge from the iron and steel industries (ISIs). While significant efforts have been made to mitigate air pollutants and carbon emissions within the ISIs, there remains a gap in comprehending the control of carbon emissions, air pollutant emissions, and their contributions to air pollutant concentrations at the enterprise level. In this study, we devised the Air Pollutant and Carbon Emission and Air Quality (ACEA) model to identify enterprises with noteworthy air pollution and carbon emissions, as well as substantial contributions to air pollutant concentrations. We constructed a detailed inventory of air pollutants and CO2 emissions from the iron and steel industry in Tangshan for the year 2019. The findings reveal that in 2019, Tangshan emitted 5.75 × 104 t of SO2, 13.47 × 104 t of NOx, 3.55 × 104 t of PM10, 1.80 × 104 t of PM2.5, 5.79 × 106 t of CO and 219.62 Mt of CO2. The ACEA model effectively pinpointed key links between ISI enterprises emitting air pollutants and carbon dioxide, notably in pre-iron-making processes (coking, sintering, pelletizing) and the Blast furnace. By utilizing the developed air pollutant emission inventory, the CALPUFF model assessed the impact of ISI enterprises on air quality in the Tangshan region. Subsequently, we graded the performance of air pollutant and CO2 emissions following established criteria. The ACEA model successfully identified eight enterprises with significant air pollution and carbon emissions, exerting notable influence on air pollutant concentrations. Furthermore, the ACEA outcomes offer the potential for enhancing regional air quality in Tangshan and provide a scientific instrument for mitigating air pollutants and carbon emissions. The effective application of the ACEA model in Tangshan’s steel industry holds promise for supporting carbon reduction initiatives and elevating environmental standards in other industrial cities across China.

Research on the air pollution reduction effect of winter clean heating policy

The international community is confronted with an urgent need to resolve the global climate crisis, which has emerged as a critical issue. China, in its capacity as a responsible major country, has proposed the “3060” goal and actively advocated for air pollution control. In northern China, a substantial quantity of coal is burned for heating in winter, leading to frequent air pollution, and seriously affecting people's health and quality of life. Clean heating is an important national livelihood strategy related to residents' heating and an essential manifestation of advancing ecological civilization construction. Based on panel data from 126 heating cities in the northern region from 2013 to 2020, this paper adopts the Difference-In-Difference (DID) model to investigate the effect and mechanism of the Winter Clean Heating Plan for Northern China on air pollution reduction. According to the research results, the policy has an abatement effect on northern region's air pollution emissions. The amount of air pollutant emissions has decreased by 17.3% since the policy's implementation. This policy has long-term effectiveness, which can become an effective measure for promoting air pollution reduction in the northern region. Secondly, economic factors, such as fiscal expenditure, and urban development factors, such as car ownership will have a driving effect on reducing air pollution, indicating that air pollution control is a systematic project that requires coordinated measures from all aspects. Thirdly, heterogeneity analysis indicates that the policy exerts a more substantial positive influence on air pollution control in Northwest China's cities and non-provincial capital cities. This indicates that cities should focus on implementing precise heating policies and not be “one size fits”. Finally, based on the theory of whole-process governance, this paper explores the indirect influence mechanism of the impact of the policy on air pollution reduction. By promoting energy structure transformation and promoting technological innovation, policy can indirectly improve the effectiveness of air pollution reduction. As an effective environmental regulation method, the policy can significantly reduce air pollution emissions in northern China, which is of great significance for the Chinese-style green modernization construction in this region.

Collaborative governance effects of carbon market on GHG and air pollutants emission reduction in 3E model —— analysis based on System Dynamics

The carbon market is regarded as one of the important means to achieve China's dual carbon target. It has ancillary effect for reducing air pollution while regulating carbon emissions since climate change and air pollution share the same origin and homology. Research on how to design the carbon market mechanism in order to maximize the synergistic effect of reducing greenhouse gas and air pollution will have a very important practical impact for China. This study conducts a theoretical analysis of the collaborative emission reduction path of China's carbon market, and constructs an Energy-Economy-Environment (3E) model of the collaborative emission reduction effect of carbon trading system based on System Dynamics. After analyzing the feedback path of the core cycle of the model and verifying its performance, three main policy factors in the carbon market are explored, and their effects under the dual objectives of emission control and economic development are comprehensively evaluated. This study suggests that the exploration of the potential of carbon market for collaborative governance should be accelerated, and ensure the orderly expansion of coverage and precise setting of limits, so as to ensure the smooth achievement of carbon reduction targets while guaranteeing the social and economic development.

Characteristics of inorganic acid emission from various generation semiconductor manufacturing factories

With advancements in semiconductor industry technology, the gas emissions per wafer have decreased, but the emission compositions have shown significant differences. This study analyzed nine semiconductor plants representing different generations of process technologies, ranging from 3 μm to 12 nm technology nodes. Stack inspections were conducted on the acid, alkali, and organic exhaust systems to understand the characteristics of inorganic acid emissions in plants in different process technologies. The analysis showed that with technological process and air pollution control equipment advancements, the emissions of inorganic acids per wafer decreased by 38% compared to the first generation. It is worth noting that both hydrofluoric acid and nitric acid are identified as the primary pollutants in traditional semiconductor process plants. At the same time, H2SO4 was instead the primary pollutant in advanced process plants. Based on these characteristics, each plant has established relevant improvement strategies. After two years of improvement, the emissions of inorganic acids per wafer in each generation of plants are evidenced to have further decreased by 15–56%. Hence, it is shown that these initiatives and studies have successfully helped to reduce air pollution emissions and promote advanced green manufacturing.

Relationship between air pollutants and spontaneous abortion in a coal resource valley city: a retrospective cohort study

Objective Pollutants in the atmosphere have been linked to poor pregnancy outcomes in women. However, such investigations are scarce in metropolitan northern China. The major exposure window of air pollution affecting pregnant women is also unknown. Methods For the analysis, this retrospective cohort study enrolled 6960 pregnant women recorded at Tongchuan People’s Hospital from January 2018 to December 2019. Pollutant concentration values from the nearest monitoring station to the pregnant women were used to estimate exposure doses for each exposure window. Logistic regression models were created to investigate the connection between pollutants and spontaneous abortion while controlling for confounding factors. Results PM2.5 was a risk factor for spontaneous abortion in T3 (30–60 days before the first day of the last menstrual period [LMP]), (OR: 1.305, 95% CI: 1.143–1.490) and T4 (60–90 days before the first day of the LMP),(OR: 1.450, 95% CI: 1.239–1.696) after controlling for covariates. In the same window, PM10 was a risk factor (OR: 1.308, 95% CI: 1.140–1.500), (OR: 1.386, 95% CI: 1.184–1.621). In T2 (30 days before the first day of the LMP), T3, and T4, SO2 was a risk factor for spontaneous abortion (OR: 1.185, 95% CI: 1.025–1.371), (OR: 1.219, 95% CI: 1.071–1.396), (OR: 1.202, 95% CI: 1.040–1.389). In T3 and T4, NO2 was a risk factor (OR: 1.171, 95% CI: 1.019– 1.346), (OR: 1.443, 95% CI: 1.259–1.655). In T1 (from the first day of the LMP to the date of abortion), O3 was found to be a risk factor (OR: 1.366, 95% CI: 1.226–1.521). Conclusion Exposure to high levels of air pollutants before and during pregnancy may be a risk factor for spontaneous abortion in pregnant women. This study further illustrates the importance of reducing air pollution emissions.

Variation characteristics and coordinated emission reduction of air pollutants in megacity of Chengdu-Chongqing economic circle under dual carbon goal

The air pollution process and impact mechanism of megacities located in complex terrain are particularly complex. As a typical megacity in China, Chengdu has unique topographical and meteorological conditions, and its pollution control is difficult. This study systematically elucidated the variation characteristics of six criteria air pollutants in Chengdu between 2014 and 2020. Besides, the PM<sub>2.5</sub>/PM<sub>10</sub> and NO<sub>2</sub>/SO<sub>2</sub> ratios were discussed. Furthermore, a detailed analysis of the correlation between air pollutants was carried out. Finally, the collaborative path of carbon reduction and air pollution control is discussed. The results indicated that SO<sub>2</sub>, PM<sub>2.5</sub>, PM<sub>10</sub> and CO were significantly decreased by 62.9%, 50.8%, 45.5%, and 36.7%, respectively. PM<sub>2.5</sub> and O<sub>3</sub> compliance rates are very low, and O<sub>3</sub> increases with fluctuations. SO<sub>2</sub>, NO<sub>2</sub>, CO, PM showed a “U-shaped” seasonal variation, and there was a “seesaw” phenomenon between O<sub>3</sub> and PM<sub>2.5</sub>. The continuous changing trends also found in the ratios of PM<sub>2.5</sub>/PM<sub>10</sub> and NO<sub>2</sub>/SO<sub>2</sub>. The results highlight the importance of coordinated reduction of carbon emissions and pollutants in Chengdu. This research can improve the prediction accuracy of air pollution in complex terrain areas under global warming, and improve the understanding of the formation mechanism of air pollution in special terrains around the world.

Air pollution, mortality, and hospital admissions in Scotland: A 16 years register-based study

Abstract Background Air pollution is associated with poor health and higher mortality. However, studies that link high spatial resolution air pollution data for several pollutants to individual-level data over prolonged period (>10 years) and assess multiple health outcomes are limited. In this study, we investigated the association between 16-years exposure to air pollution and all-cause and cause-specific (cardiovascular, respiratory, cancer, infectious, and mental/behavioural disorders) mortality and hospital admissions in Scotland. Methods Individual-level data from the “Scottish Longitudinal Study” for 202,237 individuals (2002-2017) were linked to yearly concentrations of NO2, SO2, PM10, and PM2.5 pollutants at 1-Km2 spatial resolution using the individual's residential postcode. The association between air pollution and mortality and hospital admissions was examined using Cox Proportional-Hazards and multilevel mixed-effects negative binomial models, respectively. Results Increasing concentrations of PM2.5, PM10, and NO2 pollutants were associated with higher rates of all-cause, cardiovascular, respiratory, cancer, and infectious mortality and hospital admissions. Mortality from respiratory diseases increased by 6.2% (95%CI=2.8%−9.6%), 2.5% (95%CI=0.5%−4.5%), and 1.2% (95%CI=0.5%−1.9%) per 1 µg/m3 increase in PM2.5, PM10 and NO2 pollutants, respectively. Exposure to SO2 was mainly linked to mental/behavioural disorders mortality (HR = 1.05; 95%CI=1.02-1.07) and respiratory hospital admissions (IRR=1.02; 95%CI=1.01-1.03). Conclusions This study revealed a positive association between air pollution and mortality and hospital admissions in Scotland. Interventions on air pollution through stricter environmental regulations could help ease the mortality and hospital admission burden, for both physical and mental illness. Key messages • Air pollution is associated with a higher risk of mortality and hospital admissions for both physical and mental health-related outcomes. • Policymakers are urged to provide incentives for the usage of greener energy sources to reduce air pollution emissions.

Comparing and optimizing municipal solid waste (MSW) management focused on air pollution reduction from MSW incineration in China

China is the largest developing country in the world, and its municipal solid waste (MSW) has increased with a compound annual growth rate of 5.1 % since 1980. Incineration, which has the advantages of mass- and volume-reduction as well as energy and heat recovery, has become the mainstream environmentally sound treatment method in China. However, air pollution emissions are the primary reason for limiting MSW incineration (MSWI). Currently, the Chinese government is devoted to comprehensively implementing MSW classification. However, the classification model and the future MSW reduction rate are not yet clear. In this study, we project scenarios of air pollution emissions until 2030 based on the different MSW classification models (MSW reduction rates) and diffusion rates of ultra-low emission technology. A total of 6011 tons (t) of particulate matter, 25,881 t of SO2, 14,915 t of CO, 17,167 t of HCl, and 200,166 t of NOx will be emitted in 2030 under the business-as-usual (BAU) scenario, and air pollutants will not peak under this scenario. Air pollutants will reduce by 11 % of the BAU scenario by only implementing an MSW reduction of 20 % (JPN-model). The optimal scenario (DEU-model, increasing the efficiency of material recovery and upgrading air pollution control devices) means that air pollutants will be reduced by 83.2–96.2 % from the base amount under the BAU scenario. These results provide references for MSW management and air pollution emission reduction from the aspects of MSW classification and technology upgrades in China.

Experimental Study on Prediction for Combustion Optimal Control of Oil-Fired Boilers of Ships Using Color Space Image Feature Analysis and Support Vector Machine

The International Maritime Organization strives to improve the atmospheric environment in oceans and ports by regulating ship emissions of air pollutants and promoting energy efficiency. This study deals with the prediction of eco-friendly combustion in boilers to reduce air pollution emissions. Accurately measuring air pollutants from ship boilers in real-time is crucial for optimizing boiler combustion. However, using data obtained through an exhaust gas analyzer for real-time control is challenging due to combustion process delays. Therefore, a real-time predictive modeling approach is proposed to enhance the accuracy of prediction models for NOx, SO2, CO2, and O2 by analyzing the color spectrum of flame images in a quasi-instantaneous combustion state. Experimental investigations were carried out on an oil-fired boiler installed on an actual ship, where the air damper was adjusted to create various combustion conditions. This algorithm is a saturation-based feature extraction filter (SEF) through color spectrum analysis using RGB (red, green, and blue) and HSV (hue, saturation, and value). The prediction model applying the proposed method was verified against exhaust gas analyzer data using a new data set, and real-time prediction performance and generalization were confirmed.

Effects of air environmental audit on reducing air pollutant emissions: evidence from China.

In order to mitigate air pollution, governments have implemented various active measures for air pollution prevention and control, among which the air environmental audit is an essential supervision initiative of air environmental regulation policy. This study aims to focus on and investigate the role of air environmental audit in national governance systems by collecting panel data on air environmental audits and air pollutant emissions from 261 prefecture-level cities across China between 2004 and 2018. Using difference-in-differences (DID) models, we empirically analyzed the policy effects of air environmental audits on reducing air pollutant emissions. The results indicate that air environmental audits have a significant impact on reducing air pollutant emissions, which is robust under multiple scenarios, including propensity score matching and placebo tests. The effect of air environmental audits varies significantly among different air pollutants, with the most significant and rapid effect observed on PM2.5 concentration, while industrial sulfur dioxide and industrial smoke (dust) emissions exhibit a time lag. Moreover, the promotion effect of air environmental audits on air pollution control displays strong heterogeneity based on local economic levels, initial environmental quality, and government competition. Therefore, it is of great significance to intensify the implementation of air environmental audits for air pollution control and to promote and improve the audit work according to pollutant classification and local conditions, thereby fully leveraging the audit's role and further improving air quality continuously.

Plants' emission behaviors under dual control of pollutant concentration and quantity

Quantity-based and concentration-based limits are two common environmental permitting approaches utilized by government worldwide in environmental management. While existing literature is still unambiguous about roles played by quantity-based versus concentration-based limits in environmental management, it becomes evident that relying exclusively on concentration-based or quantity-based limits to control plant emissions may not necessarily result in improved environmental quality. This paper leverages a unique opportunity arising from a recent reform in China's Pollutant Emission Permit System (PEPS) initiated in 2016 to analyze how the introduction of quantity-based limits in addition to concentration-based limits through the PEPS reform impact emissions at the plant level. Utilizing a unique plant-level continuous emission monitoring system data collected from Shaanxi Province (located in western China), the paper finds a significant reduction in air pollutant emissions as a result of the PEPS reform (nitrogen oxides (NOx) by 39%, sulfur dioxide (SO2) by 15% and particulate matter (PM) by 13%). The heterogeneity analyses show emission reductions in plants differ across those with varying quantity limits specified in their permits, distinct emission ratios and diverse ownership structures. Furthermore, plants that fall under the classified management system with more stringent regulations imposed, especially those operating in high-pollution sectors, situated within industrial parks, or classified as large-sized plants, attain higher pollutant quantity limits. Findings of the paper carry important implications for effective environmental management, particularly within developing countries, and shed some light on carbon emission reduction policies in China.

Benefits, risks, barriers, and facilitators to cycling: a narrative review.

There is large potential to increase cycling participation worldwide. Participation in cycling is associated with lower risk of mortality from any cause, and incidence of cardiovascular disease and type 2 diabetes, as well as positive mental health and well-being. The largest potential for health gains likely to come from increasing participation amongst those who do not currently cycle regularly, rather than encouraging those who already cycle regularly to cycle more. Replacing car journeys with cycling can lead to reductions in air pollution emissions and lower pollutant exposure to the general population. Important gaps and uncertainties in the existing evidence base include: the extent to which the health benefits associated with cycling participation are fully causal due to the observational nature of much of the existing evidence base; the real-world economic cost-benefits of pragmatic interventions to increase cycling participation; and the most effective (combination of) approaches to increase cycling participation. To address these uncertainties, large-scale, long-term randomised controlled trials are needed to: evaluate the effectiveness, and cost-effectiveness, of (combinations of) intervention approaches to induce sustained long-term increases in cycling participation in terms of increases in numbers of people cycling regularly and number of cycling journeys undertaken, across a range of population demographic groups; establish the effects of such interventions on relevant outcomes related to health and wellbeing, economic productivity and wider societal impacts; and provide more robust quantification of potential harms of increasing cycling participation, such as collision risks.

A Deep Learning-Based Multi-objective Optimization Model for PM2.5 Prediction

Air pollution caused by particulate matter with a diameter of less than 2.5 μm (PM2.5) poses a serious threat to human health and the environment. Predicting PM2.5 concentrations and controlling emissions are crucial for pollution prevention and control. This study proposes a comprehensive solution based on weight-sharing deep learning and multi-objective optimization. The proposed approach first utilizes a model that combines the Convolutional Neural Network and Long Short-Term Memory Neural Network to analyze data from 13 air quality monitoring stations in Xi'an City. By simultaneously inputting data from different monitoring stations, the model can extract highly correlated spatiotemporal features, enabling accurate predictions of PM2.5 concentrations for specific monitoring stations using LSTM. In addition, a multi-objective optimization model is established with the primary goal of achieving maximum total emission reduction. This model takes into account four key factors: the total emission reduction, the task of emission reduction, the government subsidy, and the total cost of emission reduction. To obtain the emission reduction of PM2.5 concentration at 13 monitoring stations, 5 classical intelligence algorithms are employed to solve the model. Experimental results demonstrate the effectiveness of the proposed prediction model, with an average Root Mean Square Error (RMSE) of 12.820 and a fitting coefficient (R2) of 0.907, outperforming all comparison models. The proposed model exhibits strong generalization ability, making it applicable to different time and space conditions. Furthermore, it can be adapted for calculating emission reduction of other air pollutants. Lastly, the multi-objective optimization model achieves significant success in terms of total emission reduction. This study provides a new reference in the field of artificial intelligence and its application to air pollution control. The findings hold great significance for promoting public health and environmental protection.

Is there an EKC between economic growth and air pollutant emissions in SAARC countries? Evidence from disaggregated analysis.

The manufacturing and construction (M&C) sector not only plays a vital role in promoting economic growth, but is also a significant contributor to global air pollution. Growing concerns regarding air pollutant emissions necessitate a more disaggregated (i.e., sectoral) investigation in order to identify the major contributors. This study employs aggregated and disaggregated data to determine the fundamental effects of economic growth (i.e., overall growth and sectoral growth) on air pollutant emissions (APE) (specifically, PM2.5 and PM10 released by the M&C sector) in SAARC economies between 1995 and 2018. It assesses the environmental Kuznets curve (i.e., inverted U-shaped and N-shaped) using the feasible generalized least squares (FGLS), panel-corrected standard errors (PCSE), and generalized method of moments (GMM) techniques. The sectoral analysis reveals the presence of an N-shaped EKC while the overall analysis indicates an inverted U-shaped EKC. Population, financial development (FD), and merchandise exports (MX) have no influence on the estimates. Population and FD increase APE in all models, whereas the effects of MX vary between models. As SAARC economies are capital-deficient, these economies can adopt unbalanced environmental protection policies. First, focus on major contributing sectors (e.g., M&C sector) to curb APE, then focus on less emitting sectors in turn. By implementing pollution reduction strategies on M&C sector activities, governments may reach their threshold (peak) points earlier than expected. A reduction in APE is impossible without rigorous monitoring and application. Being capital-deficient nations and given the collective nature of the problem, a Transboundary Haze/Pollution agreement is required to solve this issue.

Regional production restriction policy and firms’ green transition: Evidence from Beijing-Tianjin-Hebei region

Taking the regional production restriction policy in the Beijing-Tianjin-Hebei region and surrounding areas (BTH2+26) -- Chinese off-peak production (COPP) policy in heating season from 2017 -- as a quasi-natural experiment, this paper adopts the DID method to explore how the COPP policy impacts firms' green transition from the dual perspective of pollutant emissions reduction and efficiency gains and its internal mechanism. The results show that: (1) The COPP policy can significantly reduce air pollutant emissions without decreasing the TFP of Chinese heavy-polluting listed companies in BTH2+26. (2) Mechanism analysis find that the COPP policy can achieve firms’ green transition that combined emission reduction and efficiency gains, which is due to the promotion effect of technological innovation activities and the reduction effect of environmental costs. (3) Further analysis shows that the COPP policy on the innovation output ability of Chinese heavy-polluting companies in BTH2+26 is greater than that on the innovation input ability. (4) Heterogeneity analysis shows that the COPP policy has a more significant promoting effect on the green transition of private enterprises. Our paper can provide empirical evidence and policy recommendation for the Chinese government to promote pollution control in specific regions.

Pathway and Policy for China's Provincial Carbon Emission Peak

An effective way for China to achieve a carbon emission peak by 2030 is to encourage developed regions to take the lead in attaining carbon peaking at the regional level. Considering Jiangsu Province as an example, this study established a provincial low emissions analysis platform (LEAP-Jiangsu) model. It combined the improved multilevel logarithmic mean Divisia index (M-LMDI) model, Tapio decoupling model, and the synergistic effect of pollution and carbon reduction model to explore the key influencing factors of carbon emissions and carbon reduction paths. The improved M-LMDI model was used to analyze the factors influencing historical and future carbon emissions in Jiangsu Province. Based on the analysis results and planning objectives, a LEAP-Jiangsu model involving various development scenarios was established to predict the time and value of carbon emission peaks. The Tapio decoupling and synergistic effect models were used to clarify the relationship between carbon emissions and economic development, the synergistic effect of carbon, and air pollutant emission reduction. The prediction results demonstrated that the total primary energy demand of Jiangsu Province in 2035 was predicted to be approximately 401.2-474.6 Mt, and the final energy demand would be approximately 319.2-382.3 Mt. Jiangsu Province was most likely to achieve the goal of carbon peaking in 2025-2030, and the peak carbon emission was approximately 815.3-845.7 Mt. The contribution rates of energy conservation and emission reduction measures such as energy intensity reduction, industrial structure optimization, terminal electrification improvement, and energy structure adjustment were 33.1%, 26.8%, 21%, and 15.2%, respectively.

Assessing the co-benefits of emission reduction measures in transportation sector: A case study in Guangdong, China

The transportation sector is becoming a major contributor to air pollutants and greenhouse gas emissions, and a key sector for emission control. Given the relevant pollutant reduction measures have been in place, policymakers must quantify mitigation potential and synergies of existing policies to determine further measures. In this study, we aim to explore the potential and co-effects of transportation sector abatement policies on air pollutants and CO2 emissions during 2020–2060 in Guangdong. To more thoroughly assess the effectiveness of the policies, we also conducted analysis within transportation sub-sectors. The findings show that the co-benefit effects differ among various pollutants and sub-sectors. The strengthening emission standards scenario has the greatest short-term effect on reducing air pollutant emissions but no effect on CO2 as there is a lack of CO2 emission control standards. The optimising energy structure scenario presented the largest potential abatement, with a 60–90% reduction compared to the business-as-usual scenario in 2060. However, there are potential emission transfers, with SO2 (1.2 kt) and PM2.5 (1.5 kt) emission reductions, from electric vehicles to the power sector. Moreover, emission transfers among transportation sub-sectors were also found in the optimising transport modes scenario.

Mitigation synergy and policy implications in urban transport sector: a case study of Xiamen, China

The urban transport sector is one of most significant contributors to greenhouse gas (GHG) and air pollutant (AP) emissions. To achieve co-benefits of GHG and AP emission reductions, a synergistic mitigation approach targeting both climate change and air pollution has gained more attention. In this study, we evaluate mitigation synergy and policy implications for GHGs and nine APs, namely, sulfur dioxide (SO2), nitrogen oxides (NO x ), carbon monoxide (CO), particulate matters (PM10 and PM2.5), black carbon (BC), organic carbon (OC), volatile organic compounds (VOCs) and ammonia (NH3), in the transport sector of Xiamen, China, during the 2013–2060 period using the Low Emissions Analysis Platform model and quantitative analysis methods. Results show that light-duty vehicles, river boats, buses and heavy-duty trucks are significant common sources of GHG and AP emissions. Road sector abatement during 2013–2020 was most prominent, especially for CO, NO X , VOCs and GHGs. In this sector, guide green travel (GGT) and adjust energy structure (AES) are dominant measures for mitigation synergy between GHGs and APs. From 2021 to 2060, emission pathways for GHGs, SO2, CO, VOCs and NH3 under optimize transport structure (OTS), AES and GGT scenarios will decrease markedly. Their emissions will peak soon relative to those under business as usual scenario. Additionally, the potential of mitigation synergy may mainly be attributed to the road and shipping sectors under AES scenario, which is the most effective in reducing PM10, PM2.5, BC and OC emissions; the mitigation potential under the AES scenario for GHGs and other APs is nearly 1–4 times as high as that under OTS and GGT scenarios. Therefore, mitigation synergy, especially in adjusting the energy structure for the transport sector, is essential for achieving the simultaneous goals of the ‘blue sky’ and ‘carbon peaking and neutrality’.

The worldwide COVID-19 lockdown impacts on global secondary inorganic aerosols and radiative budget.

Global lockdown measures to prevent the spread of the coronavirus disease 2019 (COVID-19) led to air pollutant emission reductions. While the COVID-19 lockdown impacts on both trace gas and total particulate pollutants have been widely investigated, secondary aerosol formation from trace gases remains unclear. To that end, we quantify the COVID-19 lockdown impacts on NOx and SO2 emissions and sulfate-nitrate-ammonium aerosols using multiconstituent satellite data assimilation and model simulations. We find that anthropogenic emissions over major polluted regions were reduced by 19 to 25% for NOx and 14 to 20% for SO2 during April 2020. These emission reductions led to 8 to 21% decreases in sulfate and nitrate aerosols over highly polluted areas, corresponding to >34% of the observed aerosol optical depth declines and a global aerosol radiative forcing of +0.14 watts per square meter relative to business-as-usual scenario. These results point to the critical importance of secondary aerosol pollutants in quantifying climate impacts of future mitigation measures.

Air quality and related health impact in the UNECE region: source attribution and scenario analysis

Abstract. The TM5-FAst Scenario Screening Tool (TM5-FASST) was used to study the influence of abatement policies within and outside the United Nations Economic Commission for Europe (UNECE) region on the exposure to O3 and PM2.5 and associated mortality in the UNECE countries. To that end, the impacts of pollutants derived from different geographic areas and activity sectors were evaluated using ECLIPSE V6b scenarios of air pollutant and greenhouse gas (GHG) emission reduction. The mortalities were attributed to O3 and PM2.5 following the Global Burden of Disease (GBD) approach and allocated to geographic areas (UNECE and non-UNECE) and activity sectors, including natural sources. In addition, a combination of runs designed for the purpose led to allocating exposure to O3 and related mortality to two families of precursors: NOx–VOC and CH4. In this study, the baseline scenario (current legislation scenario, CLE), which assumes that all air quality and greenhouse gas abatement measures adopted by 2018 are fully implemented, is compared with more ambitious scenarios (maximum feasible reduction, MFR). The findings from this comparison indicate that O3 exposure within the UNECE area is more sensitive to measures outside the UNECE region than PM2.5 exposure, even though the latter leads to higher mortality than the former. In the CLE, the mortality associated with O3 exposure in the UNECE region grows steadily from 2020 to 2050. The upward trend is mainly associated with the growing impact of CH4 emissions from areas outside UNECE. Also, the mortality related to NOx–VOC emissions outside UNECE increases in the same period. By comparison, a measurable decrease (13 %) is observed in the mortality attributable to NOx–VOC emissions within UNECE. In the same time window, the mortality associated with PM2.5 exposure in the UNECE region decreases between 2020 and 2040 and then rises until 2050. The PM2.5-related mortality in UNECE is mainly due to anthropogenic emissions within this region followed by natural sources (sea salt and dust) mainly located outside the UNECE region. Between 2020 and 2050, the impact of some UNECE anthropogenic sources on PM2.5-related mortality decreases progressively, in particular road transport, energy production and domestic combustion, while others, namely agriculture and industry, show an upward trend. Finally, the analysis of MFR scenarios confirms that abatement measures in line with UN Sustainable Development Goals (SDGs) and the Paris Agreement can lead to significant co-benefits between air quality and climate policies.