Air Pollution Control Strategies Research Articles

The first application of a numerically exact, higher-order sensitivity analysis approach for atmospheric modelling: implementation of the hyperdual-step method in the Community Multiscale Air Quality Model (CMAQ) version 5.3.2

Abstract. Sensitivity analysis in chemical transport models quantifies the response of output variables to changes in input parameters. This information is valuable for researchers engaged in data assimilation and model development. Additionally, environmental decision-makers depend upon these expected responses of concentrations to emissions when designing and justifying air pollution control strategies. Existing sensitivity analysis methods include the finite-difference method, the direct decoupled method (DDM), the complex variable method, and the adjoint method. These methods are either prone to significant numerical errors when applied to nonlinear models with complex components (e.g. finite difference and complex step methods) or difficult to maintain when the original model is updated (e.g. direct decoupled and adjoint methods). Here, we present the implementation of the hyperdual-step method in the Community Multiscale Air Quality Model (CMAQ) version 5.3.2 as CMAQ-hyd. CMAQ-hyd can be applied to compute numerically exact first- and second-order sensitivities of species concentrations with respect to emissions or concentrations. Compared to CMAQ-DDM and CMAQ-adjoint, CMAQ-hyd is more straightforward to update and maintain, while it remains free of subtractive cancellation and truncation errors, just as those augmented models do. To evaluate the accuracy of the implementation, the sensitivities computed by CMAQ-hyd are compared with those calculated with other traditional methods or a hybrid of the traditional and advanced methods. We demonstrate the capability of CMAQ-hyd with the newly implemented gas-phase chemistry and biogenic aerosol formation mechanism in CMAQ. We also explore the cross-sensitivity of monoterpene nitrate aerosol formation to its anthropogenic and biogenic precursors to show the additional sensitivity information computed by CMAQ-hyd. Compared with the traditional finite difference method, CMAQ-hyd consumes fewer computational resources when the same sensitivity coefficients are calculated. This novel method implemented in CMAQ is also computationally competitive with other existing methods and could be further optimized to reduce memory and computational time overheads.

Wintertime investigation of PM10 concentrations, sources, and relationship with different meteorological parameters

Meteorological factors play a crucial role in affecting air quality in the urban environment. Peshawar is the capital city of the Khyber Pakhtunkhwa province in Pakistan and is a pollution hotspot. Sources of PM10 and the influence of meteorological factors on PM10 in this megacity have yet to be studied. The current study aims to investigate PM10 mass concentration levels and composition, identify PM10 sources, and quantify links between PM10 and various meteorological parameters like temperature, relative humidity (RH), wind speed (WS), and rainfall (RF) during the winter months from December 2017 to February 2018. PM10 mass concentrations vary from 180 – 1071 µg m−3, with a mean value of 586 ± 217 µg m−3. The highest concentration is observed in December, followed by January and February. The average values of the mass concentration of carbonaceous species (i.e., total carbon, organic carbon, and elemental carbon) are 102.41, 91.56, and 6.72 μgm−3, respectively. Water-soluble ions adhere to the following concentration order: Ca2+ > Na+ > K+ > NH4+ > Mg2+. Twenty-four elements (Al, Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Co, Zn, Ga, Ge, As, Se, Kr, Ag, Pb, Cu, and Cd) are detected in the current study by PIXE analysis. Five sources based on Positive Matrix Factorization (PMF) modeling include industrial emissions, soil and re-suspended dust, household combustion, metallurgic industries, and vehicular emission. A positive relationship of PM10 with temperature and relative humidity is observed (r = 0.46 and r = 0.56, respectively). A negative correlation of PM10 is recorded with WS (r = − 0.27) and RF (r = − 0.46). This study’s results motivate routine air quality monitoring owing to the high levels of pollution in this region. For this purpose, the establishment of air monitoring stations is highly suggested for both PM and meteorology. Air quality standards and legislation need to be revised and implemented. Moreover, the development of effective control strategies for air pollution is highly suggested.

Effect of air pollution on the global burden of cardiovascular diseases and forecasting future trends of the related metrics: a systematic analysis from the Global Burden of Disease Study 2021.

This study assesses the worldwide cardiovascular disease (CVD) burden attributed to air pollution, utilizing data from the Global Burden of Disease Study 2021. We explored the impact of air pollution on CVDs globally, regionally, and nationally, while considering correlations with age, gender, and socio-demographic index (SDI). A decomposition analysis was conducted to discern the contributions of aging, population growth, and epidemiological shifts to the changes in disability-adjusted life years (DALYs) from 1990 to 2021. Additionally, an ARIMA model was used to forecast the future CVD burden through 2050. In 2021, air pollution was responsible for approximately 2.46 million deaths and 58.3 million disability-adjusted life years (DALYs) attributable to CVDs, with a discernible decrease over the period studied. The greatest impacts were observed in individuals aged 75-79 and over 80, particularly among males. The decomposition analysis indicated that shifts in epidemiology were the primary factors driving these changes. Future projections suggest potential increases in mortality and DALY rates in regions with low and high-middle SDI, alongside rising age-standardized death and mortality rates in high SDI areas. These findings underscore the urgency of implementing targeted CVD prevention and air pollution control strategies to mitigate the impact on public health.

Characterization of spatial-temporal distribution and microenvironment source contribution of PM2.5 concentrations using a low-cost sensor network with artificial neural network/kriging techniques

Low-cost sensors (LCS) network is widely used to improve the resolution of spatial-temporal distribution of air pollutant concentrations in urban areas. However, studies on air pollution sources contribution to the microenvironment, especially in industrial and mix-used housing areas, still need to be completed. This study investigated the spatial-temporal distribution and source contributions of PM2.5 in the urban area based on 6-month of the LCS network datasets. The Artificial Neural Network (ANN) was used to calibrate the measured PM2.5 by the LCS network. The calibrated PM2.5 were shown to agree with reference PM2.5 measured by the BAM-1020 with R2 of 0.85, MNE of 30.91%, and RMSE of 3.73 μg/m3, which meet the criteria for hotspot identification and personal exposure study purposes. The Kriging method was further used to establish the spatial-temporal distribution of PM2.5 concentrations in the urban area. Results showed that the highest average PM2.5 concentration occurred during autumn and winter due to monsoon and topographic effects. From a diurnal perspective, the highest level of PM2.5 concentration was observed during the daytime due to heavy traffic emissions and industrial production. Based on the present ANN-based microenvironment source contribution assessment model, temples, fried chicken shops, traffic emissions in shopping and residential zones, and industrial activities such as the mechanical manufacturing and precision metal machining were identified as the sources of PM2.5. The numerical algorithm coupled with the LCS network presented in this study is a practical framework for PM2.5 hotspots and source identification, aiding decision-makers in reducing atmospheric PM2.5 concentrations and formulating regional air pollution control strategies.

Achieving health-oriented air pollution control requires integrating unequal toxicities of industrial particles

Protecting human health from fine particulate matter (PM) pollution is the ambitious goal of clean air actions, but current control strategies largely ignore the role of source-specific PM toxicity. Here, we proposed health-oriented control strategies by integrating the unequal toxic potencies of the most polluting industrial PMs. Iron and steel industry (ISI)-emitted PM2.5 exhibit about one order of magnitude higher toxic potency than those of cement and power industries. Compared with the current mass-based control strategy (prioritizing implementation of ultralow emission standards in the power sector), the proposed health-oriented control strategy (priority control of the ISI sector) could generate 5.4 times higher reduction in population-weighted toxic potency-adjusted PM2.5 exposure among polluting industries in China. Furthermore, the marginal abatement cost per unit of toxic potency-adjusted mass of ISI-emitted PM2.5 is only a quarter of that of the other two sectors under ultralow emission scenarios. We highlight that a health-oriented air pollution control strategy is urgently required to achieve cost-effective reductions in particulate exposure risks.

An Ensemble Model for PM2.5 Concentration Prediction Based on Feature Selection and Two-Layer Clustering Algorithm

Determining accurate PM2.5 pollution concentrations and understanding their dynamic patterns are crucial for scientifically informed air pollution control strategies. Traditional reliance on linear correlation coefficients for ascertaining PM2.5-related factors only uncovers superficial relationships. Moreover, the invariance of conventional prediction models restricts their accuracy. To enhance the precision of PM2.5 concentration prediction, this study introduces a novel integrated model that leverages feature selection and a clustering algorithm. Comprising three components—feature selection, clustering, and integrated prediction—the model first employs the non-dominated sorting genetic algorithm (NSGA-III) to identify the most impactful features affecting PM2.5 concentration within air pollutants and meteorological factors. This step offers more valuable feature data for subsequent modules. The model then adopts a two-layer clustering method (SOM+K-means) to analyze the multifaceted irregularity within the dataset. Finally, the model establishes the Extreme Learning Machine (ELM) weak learner for each classification, integrating multiple weak learners using the AdaBoost algorithm to obtain a comprehensive prediction model. Through feature correlation enhancement, data irregularity exploration, and model adaptability improvement, the proposed model significantly enhances the overall prediction performance. Data sourced from 12 Beijing-based monitoring sites in 2016 were utilized for an empirical study, and the model’s results were compared with five other predictive models. The outcomes demonstrate that the proposed model significantly heightens prediction accuracy, offering useful insights and potential for broadened application to multifactor correlation concentration prediction methodologies for other pollutants.

Characterization of photochemical losses of volatile organic compounds and their implications for ozone formation potential and source apportionment during summer in suburban Jinan, China

Volatile organic compounds (VOCs) undergo substantial photochemical losses during their transport from emission sources to receptor sites, resulting in serious implications for their source apportionment and ozone (O3) formation. Based on the continuous measurements of VOCs in suburban Jinan in August 2022, the effects of photochemical losses on VOC source contributions and O3 formation were evaluated in this study. The observed and initial concentrations of total VOCs (TVOC) were 12.0 ± 5.1 and 16.0 ± 7.4 ppbv, respectively. Throughout the observation period, alkenes had the most prominent photochemical losses (58.2%), followed by aromatic hydrocarbons (23.1%), accounting for 80.6% and 6.9% of the total losses, respectively. During high O3 episodes, the photochemical loss of VOCs was 6.9 times higher than that during the cleaning period. Alkene losses (exceeding 67.3%), specifically losses of isoprene, propylene, ethylene, and n-butene, dominated the total losses of VOCs during the O3 increase period. Eight sources of VOCs were identified by positive matrix factorization (PMF) based on the observed and initial concentration data (OC-PMF and IC-PMF, respectively). Concentrations of all emission sources in the OC-PMF were underestimated by 2.4%–57.1%. Moreover, the contribution of each emission source was over- or underestimated compared with that in case of the IC-PMF. The contributions of biogenic and motor vehicle exhaust emissions were underestimated by 5.3 and 2.8 percentage points, respectively, which was associated with substantial oxidation of the emitted high-reactive species. The contributions of coal/biomass burning and natural gas were overestimated by 2.4 and 3.9 percentage points, respectively, which were related to the emission of low-reactive species (acetylene, ethane, and propane). Based on our results, the photochemical losses of VOCs grossly affect their source apportionment and O3 formation. Thus, photochemical losses of VOCs must be thoroughly accounted to establish a precise scientific foundation for air-pollution control strategies.

A quantitative exploration of the interactions and synergistic driving mechanisms between factors affecting regional air quality based on deep learning

Air pollution represents a significant environmental challenge on a global scale. The exploration of factors and mechanisms influencing air quality has garnered considerable attention and produced a substantial body of research. Nonetheless, the absence of effective research tools has resulted in a limited understanding of the nonlinear interactive effects of multiple factors on air quality, as well as their patterns and spatiotemporal variability characteristics. Furthermore, the efficacy of multifactor joint control strategies in practical air pollution management remains inadequate. A novel regional air environmental quality prediction model was developed utilizing a neural network with long short-term memory (LSTM) in deep learning based on an analysis of the spatiotemporal heterogeneity of regional air environmental quality in the Yangtze River Economic Belt (YEB). The model results were attributed and interpreted by integrating the SHapley Additive explanation (SHAP) method, focusing on the possible interactive effects and synergistic driving mechanisms of PM2.5 and O3 as the main pollutants and other factors on the air quality index (AQI) in a quantitative way, respectively. The SHAP interaction values of five factor pairs, namely PM2.5 and temperature, PM2.5 and precipitation, O3 and temperature, O3 and NO2, and O3 and PM2.5, are depicted in dependence scatter plots that exhibit three distinct trajectory shapes, namely linear, U-shaped, and irregular. The linear pattern of the dependence scatter plots for PM2.5 and precipitation in the YEB, midstream, and downstream regions, as well as for O3 and temperature in the upstream region, is evident. The irregular patterns of PM2.5 and precipitation, O3 and NO2, O3 and PM2.5 in the upstream, and PM2.5 and temperature in the downstream are observed to be correspondingly related. Additionally, a U-shaped pattern is exhibited by O3 and temperature in the YEB, midstream and downstream, PM2.5 and temperature in the upstream and midstream, O3 and NO2 in the YEB, midstream and downstream, and O3 and PM2.5 in the YEB. Of the three models, the U-shaped model's identification of inflection points and corresponding quantitative determination of factor thresholds hold greater practical implications for decision-making in air environment management. This pattern suggests that the synergistic effect of factor pairs initially decreases with changes in each factor but subsequently begins to increase after reaching inflection points. It is anticipated that this research will contribute to the advancement of theoretical inquiry into the nonlinear multifactor interaction effect and synergistic driving model of regional air quality. Additionally, it is expected to furnish a valuable foundation for decision-making in the development of joint air pollution prevention and control strategies aimed at addressing compound air pollution issues.

Machine learning applications for a better demand controlled ventilation system experience in buildings: a review

PurposeAt the beginning of the Corona Virus Disease 2019 (COVID-19) pandemic, a digitalized construction environments surfaced in the heating, ventilation and air conditioning (HVAC) systems in the form of a modern delivery system called demand controlled ventilation (DCV). Demand controlled ventilation has the potential to solve the building ventilation's biggest problem of managing indoor air quality (IAQ) for controlling COVID-19 transmission in indoor environments. However, the improper evaluation and information management of infection prevention on dense crowd activities such as measurement errors and volatile organic compound (VOC) generation failure rates, is fragmented so the aim of this research is to integrate this and explore potentials with machine learning algorithms (MLAs).Design/methodology/approachThe method used is a thorough systematic literature review (SLR) approach. The results of this research consist of a detailed description of the DCV system and digitalized construction process of its IAQ elements.FindingsThe discussion revealed that DCV has a potential for being further integrated by perceiving it as a MLAs and hereby enabling the management of IAQ level from the perspective of health risk function mechanism (i.e. VOC and CO2) for maintaining a comfortable thermal environment and save energy of public and private buildings (PPBs). The appropriate MLA can also be selected in different occupancy patterns for seasonal variations, ventilation behavior, building type and locations, as well as current indoor air pollution control strategies. Furthermore, the conceptual framework showed that MLA application such as algorithm design/Model Predictive Control (MPC) integration can alleviate the high spread limitation of COVID-19 in the indoor environment.Originality/valueFinally, the research concludes that a large unexploited potential within integration and innovation is recognized in the DCV system and MLAs which can be improved to optimize level of IAQ from the perspective of health throughout the building sector DCV process systems. The requirements of CO2 based DCV along with VOC concentrations monitoring practice should be taken into consideration through further research and experience with adaption and implementation from the ventilation control initial stage of the DCV process.

The impact of aerosol-meteorology feedback on the effectiveness of emission reduction for PM2.5: A modeling case study in Northern China

The quantification of the effectiveness of anthropogenic emission control measures is crucial for future air quality policies. Meteorology plays a vital role in haze pollution, and the interactions between aerosol and meteorology have been widely studied. However, it is not fully clear how aerosol-meteorology feedback affects the effectiveness of emission reduction for PM2.5, which limits our ability of optimizing anti-pollution policies. Here, with the two-way atmospheric chemical transport model WRF-Chem, the effects of aerosol-meteorology feedback on the effectiveness of emission reduction for PM2.5 during a winter severe haze event in 2016 over the Northern China Plain (NCP) are studied. In the more polluted area of NCP (MP_NCP) during the daytime, 20% emission reduction over NCP increases near-surface downward shortwave radiation by 4.62 W/m2, 2 m temperature by 0.08 °C, boundary layer height by 7.19 m and reduces 2 m relative humidity by 0.31% and thereby alleviates worsened meteorological conditions caused by aerosol effect. As a result, in MP_NCP, 20% emission reduction without aerosol-meteorology feedback leads to a decrease of 40.49μg/m3 of near-surface PM2.5 and the above meteorological changes decrease near-surface PM2.5 concentration by 7.82μg/m3, indicating that aerosol-meteorology feedback strengthens the effectiveness of emission reduction by 19%. In the less polluted area (LP_NCP), aerosol effect induced meteorological changes decrease PM2.5 concentration by 7.57μg/m3 and 20% emission reduction without aerosol-meteorology feedback leads to a decrease of 13.15μg/m3 in near-surface PM2.5. This reveals a remarkable enhancement of 58% in the effectiveness of emission reduction, which is much larger than that in MP_NCP. Such difference can be attributed to the presence of more clouds in LP_NCP, where the decrease in liquid water path, along with the increase in the planetary boundary layer height, jointly contributes to the PM2.5 decrease. Moreover, the effect of aerosol-meteorology feedback on the effectiveness of emission reduction for PM2.5 is nonlinear. With increasing PM2.5 concentration, the aerosol-meteorology feedback induced PM2.5 reduction first increases and then stabilizes once the PM2.5 concentration exceeds 350 μg m−3. This study can provide reference for air pollution control strategies.

Food waste anaerobic digestion plants: Underestimated air pollutants and control strategy

Food waste management is an important global issue, and anaerobic digestion (AD) is a sustainable technology for treating food waste and developing a circular economy. Odor and health problems in AD plants have drawn increasing public attention. Therefore, this study investigated the odor characteristics and health risks in different workshops of food waste AD plants. At each site, the treatment capacities for kitchen and restaurant waste were 200 and 200–250 tons per day, respectively. Among the detected odorants, ethanol was the dominant component in terms of concentrations, while methanethiol, propanethiol, H2S, and acetaldehyde were the major odor contributors in different workshops. The odor contribution of propanethiol had been previously overlooked in several workshops. The unloading, pretreatment, and bio-hydrolysis workshops were identified as major areas requiring odor control. Besides odor, carcinogenic and non-carcinogenic risks commonly existed in food waste AD plants. The carcinogenic risk of acetaldehyde had been underestimated previously, and it was identified as the dominant carcinogen. Furthermore, benzene was a potential carcinogen. Non-carcinogenic risks were mainly caused by acetaldehyde, H2S, and ethyl acetate. The health risks were not always consistent with odor nuisance. Based on the odor and health risk assessments, several air pollution control strategies for food waste AD plants were proposed, including food waste source control, in-situ pollution control, and ex-situ pollution control.

Assessment of the Severity of Indoor Air Pollution Sources and Control Strategies in Printing Presses in Zaria Metropolis, Nigeria

The mitigation of indoor air pollution sources in printing facilities is crucial for ensuring healthy indoor environments and protecting the health and well-being of employees. This study assessed the severity of indoor air pollution sources and the different pollutant control strategies in use in 22 printing presses in Zaria metropolis, Kaduna State. The study adopted a quantitative method. A checklist was used to characterize the selected printing facilities and examine the severity of indoor air pollutant sources on a 5-point Likert severity scale. The instrument was also used to check the availability of Indoor Air Quality (IAQ) monitoring devices and Indoor Air Pollution (IAP) control technology/strategy currently in use in the printing facilities. Findings revealed 1 pollutant source (dust) with mean value of 3.55 to be severe, 9 others to be mild, and 11 to be non-severe. All the assessed printing presses have available compliant inks and coatings, while 100% unavailability of IAQ monitoring devices/equipment or capture systems were observed. The study concluded that by implementing effective control measures and monitoring of indoor air quality, the risk of negative health effects associated with exposure to indoor air pollutants can be minimized in the printing facilities. The study also recommended that Facility managers and policymakers must prioritize the implementation of effective control measures in printing facilities to reduce exposure to indoor air pollutants and improve IAQ, which ultimately promotes the health and well-being of press employees.

Seasonal and regional variations of atmospheric ammonia across the South Korean Peninsula

This study aimed to identify the factors causing NH3 emissions in the South Korean Peninsula and West Sea region. To analyze the trends of NH3 and other air pollutants, such as NOx, CO, and NR-PM1, we collected samples from six supersites across the peninsula, a roadside in Seoul, and the West Sea over different sampling periods, ranging from 1 month to 1 year. The highest NH3 concentrations were found at rural areas, ascribed to agricultural activities, particularly NH4NO3 decomposition at high summer temperatures. Areas with low population densities recorded the lowest NH3 concentrations, attributed to the lack of anthropogenic activities. A roadside field experiment confirmed the close link between ambient NH3 and vehicle emissions in urban regions by showing a strong correlation between CO and NOx concentrations and that of NH3. Moreover, we examined oceanic emissions near the eastern coast of South Korea in the West Sea. Long-range transportation studies confirmed that most of the pollutants (NH3, CO, and PM1) were transported by wind from the northeastern region of China. A maritime origin study showed that oceanic emissions and NH4NO3 decomposition in the atmosphere owing to high temperatures were the causing NH3 pollution. These findings provided valuable insights into the emission sources of NH3 in primary air pollutants in South Korea, highlighting the contributions of land-based and oceanic sources. Our study can help inform policymakers and stakeholders for developing effective regional air pollution control strategies.

Modelling optimal control of air pollution to reduce respiratory diseases

Respiratory diseases caused by inhalation of air pollutants are affected by seasonal changes and mitigated by air pollution control, resulting in complex dynamics. In order to investigate the effects of various factors such as random noise and air pollution control on respiratory diseases, we developed deterministic and stochastic two-dimensional coupled SIS models with multiple control measures. The proposed models and parameter estimation methods, including determinations of unknown parameter values, were used to fit the Air Quality Index (AQI) data for Xi’an city in recent 10 years. The existence of the optimal solutions for the deterministic and stochastic models were analyzed theoretically and provided to compare the parameter fitting solutions with the optimal solutions, and give theoretical support for seeking a more reasonable air pollution optimization prevention and control scheme. To show this, we conducted numerical simulations of the optimal control solution and state evolution trajectories under different weight coefficient ratios and control objectives. The results show that the stochastic optimal control problem is more consistent with the practical scenario. We also formulate the optimal control problem assuming that the control variable depends on the concentration of air pollutants. The optimal control solution reflected the periodic variation of the air pollution control strategy well. A comparison of cost values for different combinations of the three control measures illustrated that air pollution reduction is the most effective control measure.

Characteristics, sources and health risk assessment of trace metals and polycyclic aromatic hydrocarbons in PM2.5 from Hefei, China.

Trace metals (TRs) and polycyclic aromatic hydrocarbons (PAHs) are major toxic components of fine particulate matter (PM2.5) and related to various health adverse outcomes. The study aims to get a better understanding of the contents, sources and risks of PM2.5-bounded TRs and PAHs in Hefei, China, during the period of 2019-2021. We collected 504 samples and measured twelve TRs and sixteen priority PAHs by inductively coupled plasma mass spectrometry and high-performance liquid chromatography. The annual mass concentrations of PM2.5 was fluctuated in the year of 2019-2021 at 50.95, 47.48 and 59.38μg/m3, with seasonal variations in rank order of winter > spring > autumn > summer. The median concentrations of PM2.5-bounded ƩTRs and ƩPAHs were also fluctuated, 132.85, 80.93 and 120.27ng/m3 for ƩTRs, 2.57, 5.85 and 2.97ng/m3 for ƩPAHs, in the year of 2019, 2020 and 2021, respectively. Seasonal variations of ƩTRs and ƩPAHs show the highest concentration in winter. Positive matrix factorization was used for identified pollution emission sources, and TRs mainly originated from coal combustion, traffic emission and fugitive dust, while PAHs stemmed from biomass, diesel, gasoline and coal combustion. Health risk assessment indicated that adults were more vulnerable than children, the carcinogenic risk assessment of As and Cr manifested a certain degree of cancer risk (1.0 × 10-6 < CR < 1.0 × 10-4) in adults group, and health risks of TRs were higher than PAHs in Hefei. These findings suggest that PM2.5-bounded TRs and PAHs should be considered when making emission control strategies for air pollution, and winter, combustion sources and adults should achieve more policy attention to decrease exposure risks in Hefei.

Review on Source Profiles of Volatile Organic Compounds (VOCs) in Typical Industries in China

The source profile of volatile organic compounds (VOCs) is essential for establishing reactivity- and toxicity-based emission inventories and developing effective air pollution control strategies. In this paper, the establishment of VOC source profiles and the VOC emission characteristics are reviewed in the petrochemical, solvent use, and chemical industries, and the most up-to-date profiles of the three industries in China are compiled via necessary adjustment and reconstruction of the test data from the literature. Alkanes dominated and OVOCs were often neglected in the overall petrochemical industry and refined processes. They accounted for 60.6% and 3.2% in the merged profiles. Aromatics and OVOCs dominated in the industrial solvent use industry. OVOCs were the most prevalent in the printing and dyeing industries, furniture manufacturing industries, and automobile coating process, whereas aromatics were major contributors of the total VOCs in metal surface coating, shipping coating, and other surface coating industries in the merged profiles. A wide range of products and limited profile studies were obtained in chemical industry, and the compositions of VOCs varied significantly in the production of 30 products in the merged profile. The future research directions of VOC source profiles are discussed, mainly focusing on the sampling, establishment, and evaluation of VOC profiles.

Health Risk Assessment of Exposure to Air Pollutants Exceeding the New WHO Air Quality Guidelines (AQGs) in São Paulo, Brazil.

We applied the AirQ+ model to analyze the 2021 data within our study period (15 December 2020 to 17 June 2022) to quantitatively estimate the number of specific health outcomes from long- and short-term exposure to atmospheric pollutants that could be avoided by adopting the new World Health Organization Air Quality Guidelines (WHO AQGs) in São Paulo, Southeastern Brazil. Based on temporal variations, PM2.5, PM10, NO2, and O3 exceeded the 2021 WHO AQGs on up to 54.4% of the days during sampling, mainly in wintertime (June to September 2021). Reducing PM2.5 values in São Paulo, as recommended by the WHO, could prevent 113 and 24 deaths from lung cancer (LC) and chronic obstructive pulmonary disease (COPD) annually, respectively. Moreover, it could avoid 258 and 163 hospitalizations caused by respiratory (RD) and cardiovascular diseases (CVD) due to PM2.5 exposure. The results for excess deaths by RD and CVD due to O3 were 443 and 228, respectively, and 90 RD hospitalizations due to NO2. Therefore, AirQ+ is a useful tool that enables further elaboration and implementation of air pollution control strategies to reduce and prevent hospital admissions, mortality, and economic costs due to exposure to PM2.5, O3, and NO2 in São Paulo.

Advances in Simulating the Global Spatial Heterogeneity of Air Quality and Source Sector Contributions: Insights into the Global South.

High-resolution simulations are essential to resolve fine-scale air pollution patterns due to localized emissions, nonlinear chemical feedbacks, and complex meteorology. However, high-resolution global simulations of air quality remain rare, especially of the Global South. Here, we exploit recent developments to the GEOS-Chem model in its high-performance implementation to conduct 1-year simulations in 2015 at cubed-sphere C360 (∼25 km) and C48 (∼200 km) resolutions. We investigate the resolution dependence of population exposure and sectoral contributions to surface fine particulate matter (PM2.5) and nitrogen dioxide (NO2), focusing on understudied regions. Our results indicate pronounced spatial heterogeneity at high resolution (C360) with large global population-weighted normalized root-mean-square difference (PW-NRMSD) across resolutions for primary (62-126%) and secondary (26-35%) PM2.5 species. Developing regions are more sensitive to spatial resolution resulting from sparse pollution hotspots, with PW-NRMSD for PM2.5 in the Global South (33%), 1.3 times higher than globally. The PW-NRMSD for PM2.5 for discrete southern cities (49%) is substantially higher than for more clustered northern cities (28%). We find that the relative order of sectoral contributions to population exposure depends on simulation resolution, with implications for location-specific air pollution control strategies.

Independent risk evaluation associated with short-term black carbon exposure on mortality in two megacities of Yangtze River Delta, China

The adverse health effects of PM2.5 have been well demonstrated by many studies. However, as a component of PM2.5, evidence on the mortality risk of black carbon (BC) is still limited. In this study, based on the data of daily mean PM2.5 concentration, BC concentration, meteorological factors, total non-accidental (all-cause) and cardiovascular mortality in Shanghai and Nanjing during 2015–2016, a semi-parameter generalized additive model (GAM) in the time series and the constituent residual approach were employed to explore the exposure-response relationship between BC and human mortality in these two megacities of Yangtze River Delta, China. The main objective was to separate the health effects of BC from total PM2.5, and compare the difference of mortality ER related to BC original concentration and adjusted concentration after controlling PM2.5. Results showed that there were all significantly associated with daily mortality for PM2.5 and BC. The percentage excess risk (ER) increases in all-cause and cardiovascular categories were 1.68 % (95 % s 1.28, 2.08) and 2.16 % (95 % CI: 1.54, 2.79) with 1 μg/m3 increment in original BC concentration in Shanghai. And the ER in Nanjing was smaller than that in Shanghai. After eliminating PM2.5 confounding effects by a constituent residual approach, the BC residual concentration still had a strong significant ER. The ER for BC residual in Shanghai got an obvious increase, and ER of the cardiovascular mortality for all, females and males increased by 0.55 %, 1.46 % and 0.62 %, respectively, while the ER in Nanjing decreased slightly. It also revealed that females were more sensitive to the health risk associated with short-term BC exposure than males. Our findings provide additional important evidence and ER for mortality related to independent BC exposure. Therefore, BC emission reduction should be paid more attention in air pollution control strategies to reduce BC-related health burdens.

On prediction of air pollutants with Takagi-Sugeno models based on a hierarchical clustering identification method

In recent years, air pollution has attracted considerable attention worldwide. As an effective air protection method, the accurate prediction of air pollutants can help provide an early warning against harmful air pollutants and plan air pollution prevention and control strategies. Due to its high prediction capability, a novel method using a Takagi-Sugeno fuzzy model is presented to predict air pollutants in this study. This work investigates fuzzy model identification based on a larger dataset, and a hierarchical clustering-based identification method with model structure selection is proposed. The novelty of this work is as follows: First, a novel hierarchical clustering method has been employed in air quality forecasting. This method improves the widely used BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) by introducing a refinement phase for handling clusters with arbitrary shapes. Second, a cluster validity measure automatically determines the number of clusters. Then, the estimation of the model order selection and the model parameters is formulated as a sparse optimization problem, which can be solved using global optimization. Finally, the proposed Takagi-Sugeno model can learn the local trend pattern and spatial-temporal dependencies of multivariate air quality-related time series data. The presented method is demonstrated with air quality measurements in Shanghai, and the results show that the proposed method can achieve acceptable prediction performance.