Search

The emissions from South Asia and Southeast Asia significantly impact air quality and meteorological conditions in China. However, the individual or joint contributions of anthropogenic emissions and/or biomass burning from outside China to surface particulate matter with aerodynamic diameters less than 2.5 μm (PM2.5) and aerosol optical depth (AOD) over southern China have not been fully investigated. Here, five experiments were designed to investigate the impacts of these emissions in January (winter), March (pre‐monsoon), and October (post‐monsoon) for 2017. Aerosols from South Asia and Southeast Asia contributed less to southern China during winter and post‐monsoon seasons, whereas the wind patterns and emission intensity during the pre‐monsoon season were conducive to the transport of aerosols. During pre‐monsoon season, the total emissions contributed approximately 5.0 μg m−3 to surface PM2.5 in Xizang Province. Biomass burning in Southeast Asia increased PM2.5 in Yunnan Province by 37.9 μg m−3, while anthropogenic emissions increased it by 8.9 μg m−3. Transboundary aerosols can be transported to Xizang Province and Yunnan Province, primarily influencing PM2.5 below 2 km height. It mainly affected PM2.5 levels above the planetary boundary layer over southeast China. Aerosols from outside China can account for 79.5% and 54.8% AOD in Yunnan Province and southeast China, respectively. These aerosols reduced surface incident solar radiation by approximately 6%, leading to decreases in air temperature, wind speed, and boundary layer height. The findings are only applicable to the pre‐monsoon season. Plain Language Summary: Air pollution in South Asia and Southeast Asia is closely related to anthropogenic emissions and biomass burning. Under the influence of prevailing winds, these emissions can affect air quality over southern China during different seasons. This study combines ground observations, satellite data, and chemical transport models to investigate the impacts of anthropogenic emissions and/or biomass burning from South Asia and Southeast Asia on air quality and meteorological conditions over southern China in January, March, and October for 2017. Key Points: Transportation from South Asia and Southeast Asia account for up to 50% PM2.5 concentrations over Xizang Province in ChinaAerosols transported from South Asia and Southeast Asia can impact PM2.5 at heights up to 6 km over southern China in pre‐monsoon seasonImpacts on the surface solar radiation, air temperature, relative humidity, wind speed and planetary boundary layer height are quantified [ABSTRACT FROM AUTHOR]

Aerosol remote sensing typically relies on reflected shortwave radiation and thus lacks nighttime retrievals. Here we made an original attempt to retrieve nighttime aerosol optical depth (AOD) by utilizing longwave measurements in the atmospheric window region from the Atmospheric InfraRed Sounder (AIRS) instrument. A machine‐learning based algorithm is developed using AIRS longwave radiance and auxiliary data as the input and AOD from Moderate Resolution Imaging Spectroradiometer (MODIS) as well as reanalysis surface temperature as the output. Independent validation indicates good agreement with lunar AOD derived from surface photometers. An overall increase in nighttime AOD compared to daytime is also uncovered, which is further corroborated by surface and space lidar measurements. The theoretical basis of the algorithm is further verified using radiative transfer simulations. Our approach substantially extends the potential of hyperspectral longwave measurements and offers valuable insights into nighttime aerosol properties. Plain Language Summary: Aerosol optical depth (AOD) is an important parameter for understanding the effects of aerosols on the Earth's atmosphere and air quality. However, current satellite‐based methods for estimating AOD mainly rely on reflected sunlight, thus lacking the capability of deriving aerosol properties at night. In this study, we develop a novel machine‐learning based method for estimating nighttime AOD using longwave far infrared measurements from the Atmospheric InfraRed Sounder (AIRS) satellite instrument. Our algorithm shows satisfactory performance with good agreements with surface AOD derived from moonlight, as well as from space lidar. Through radiative transfer simulations, we were able to verify the theoretical basis of our method and establish a relationship between AOD and the longwave measurements. This new approach greatly expands our ability to estimate AOD during nighttime and provides valuable insights into day‐time aerosol variability. Key Points: Nighttime AOD is successfully retrieved from satellite longwave radiance measurementsResults agree well with AERONET lunar AOD and CALIPSO nighttime AODNighttime AOD is found to be overall higher than daytime AOD globally [ABSTRACT FROM AUTHOR]

Particulate matter (PM) and the microorganisms of duck houses may have negative impacts on animal and human health. During 2021-2022, PM2.5 and PM10 inside and outside the duck house were sampled with a built-in air sampler in Tai'an City, Shandong Province, and the diversity and abundance of microorganisms within the PM were analyzed by macrogenomic and absolute sequence analysis. The results showed that PM2.5 and PM10 concentrations in the house and at downwind points exceeded the short-term (24 h) guideline of the global air quality guidelines (AQG). Macrogenome sequencing showed that the microbial composition of the PM2.5 samples was dominated by bacteria (exceed 85%); a total of 1316 bacterial genera and 110 fungal genera were identified in PM2.5 samples from duck house 1 in winter, which were much higher than the results of amplicon sequencing method reported before, and relatively high levels of the pathogenic bacteria (Coccidioides immitis, etc.) and the conditionally pathogenic bacterium (Rothia nasimurium) were identified at the species level. Absolute quantitative sequencing detected conditionally pathogenic bacteria and allergens at high levels in PM10 samples: Corynebacterium (5.6 × 10 7 copies/g), Aerococcus (9.9 × 10 6 copies/g), Alternaria (3.3 × 10 6 copies/g), and Aspergillus (8.3 × 10 5 copies/g). Moreover, Corynebacterium was the highest content of PM10 in summer and PM2.5 samples in winter, and its pathogenicity and potential threat should be noted. The diversity and relative abundance of microorganisms were similar in the duck house and at the downwind point. The results showed that the microorganisms in the house environment have a greater influence on the air environment around the downwind point and may pose a public health risk to the staff and the surrounding area. [ABSTRACT FROM AUTHOR]

Ambient fine particulate matter (PM2.5) pollution is a major health risk. Networks of low-cost sensors (LCS) are increasingly being used to understand local air pollution variation. However, measurements from LCS have uncertainties which can act as a potential barrier for effective decision-making. LCS data thus need to be calibrated to obtain better quality PM2.5 estimates. In order to develop correction factors, LCS are typically co-located with gold-standard reference monitors. A calibration equation is then developed that relates the raw output of the LCS as closely as possible to measurements from the reference monitor. This calibration algorithm is then typically transferred to measurements from monitors in the network. Calibration algorithms tend to be evaluated based on their performance at co-location sites. It is often implicitly assumed that the conditions at the relatively sparse co-location sites are representative of the LCS network, overall. Little work has been done to explicitly evaluate the sensitivity of the LCS network hotspot detection, and spatial and temporal PM2.5 trends to the correction method applied. This paper provides a first look at how transferable different calibration methods are using a dense network of Love My Air LCS monitors in Denver. It offers a series of transferability metrics that can be applied to other networks and offers suggestions for which calibration method would be most useful for different end goals. Finally, it develops a set of best practice suggestions on calibrating LCS networks. [ABSTRACT FROM AUTHOR]

An intense dust storm from 8 April to 11 April 2023 originated from the Gobi deserts bordering Mongolia and Inner Mongolia of China and affected most parts of northern, central, and eastern China, the Korean Peninsula, and Japan. It has a significant impact on air quality in these regions and alters the optical and microphysical properties of the aerosols. In this work, a comprehensive analysis of the storm was carried out by using reanalysis data, model simulation, satellite, and ground-based observations. The geopotential height along with the wind flow reveals that the development, transportation, and dissipation of the dust storms were closely related to the movement of the Mongolia cyclones caused by Mongolia low and its surrounding highs. The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data showed a two-layer dust vertical distribution, with the bottom one below 5 km and the upper one stretching from 5 to 10 km over the Japan Sea. The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) backward trajectory uncovered Central Mongolia being the origin of the dust storm that arrived over the Japan Sea on April 9, 2022, and the forward trajectory revealed that the dust aerosols would be further transported to the Pacific Ocean. Air quality analysis demonstrated that the regions of inner Mongolia (IMG) and Jing-Jin-Ji (JJJ) suffered the most from the dust storm events, followed by the Yangtze River Delta (YRD), while the Pearl River Delta (PRD) was hardly affected, as indicated by the regional-averaged PM 10 and PM 2.5. It was also found that the dust storm event has almost no significant influence on the other air quality indicators including CO, NO 2 , O 3 , and SO 2. Historical record tracking revealed that the springtime averaged PM 10 and PM 2.5 of the year 2023 are comparable to those in 2021 which has the record-breaking dust storm case. AERONET data indicated dramatic increments of aerosol optical depth at 550 nm (AOD550) and effective radius of total size (ReffT) in regions of Gobi and JJJ, accompanied by low Angstrom Exponent (AE), and high SSA870 values, while in the regions of Korea and Japan, minor increments of AOD550 and ReffT were found. This study provides a holistic scientific view of the dust storm which could aid in the implementation of protective measures against dust storms. • Mongolian low contributed to the formation of dust storms. • PM 2.5 and PM 10 increased dramatically during the dust storms in northern China. • Both AOD and SSA increased considerably over regions of Gobi and Jing-Jin-Ji. [ABSTRACT FROM AUTHOR]

Air quality is often assessed using particulate matter (PM) mass concentration without considering its toxicity, thus possibly leading to improper control policies or inadequate health protection. Here, we studied differences in oxidative potentials (OPs) of PM samples collected using automobile air conditioning (AC) filters from 19 global cities, as well as influences from microbial contents. Dithiothreitol (DTT) assay showed remarkable differences in the PM OPs among cities (p -values ≤ 0.001, Kruskal-Wallis test). For example, the normalized index of oxidant generation (NIOG) of PM samples in San Francisco (2.20 × 10−2, annual average PM 10 = 16 μg/m3) was found to be twice that in Beijing (1.14 × 10−2, annual average PM 10 = 135 μg/m3). Limulus amebocyte lysate (LAL) assay found that PM-borne endotoxin ranged from 12.16 EU/mg (Florianopolis, Brazil) to 2518.23 EU/mg (Chennai, India) among cities. Besides, culturing method and real-time qPCR revealed significant differences of up to ∼100-fold in both bacterial and fungal levels among 19 cities. Spearman's correlation analysis implied that PM-borne microbes such as bacteria and fungi as well as metals could strongly influence the PM OP. As an example, our results in Xi'an, China further suggest that the PM 2.5 OP evolves for a particular city over the time, which is attributable to both the urbanization and air pollution control measures. This work highlights the importance in optimizing the current air quality control measures by considering the toxicity factor and its microbial constituents. Image 1 • Ambient PM toxicity per unit of mass was shown to vary greatly with different cities across the world. • PM-borne biologicals were shown to exhibit remarkable differences across major cities, contributing to the difference of PM toxicity. • PM toxicity was shown to evolve over the time as a result of ground human activities as well as air pollution control measures. • This work highlights the need of taking into account of PM toxicity for future air pollution control efforts. [ABSTRACT FROM AUTHOR]

Chongqing is the largest megacity in southwest China and has a mountainous and humid climate. Online measurements of 96 volatile organic compound (VOC) species were performed at the three sites JYS, CJZ, and NQ, which are located in the northern, central, and southern sections of the Chongqing urban district, respectively. The measurements were performed from August to September 2015, at a time interval of 1 h. The spatiotemporal variation of VOC sources in Chongqing was characterized by combining the positive matrix factorization (PMF) model with the online measurement data. The average total VOC mixing ratios of the CJZ, NQ, and JYS sites were 41.2, 34.1, and 23.0 ppbv, respectively. The mixing ratios of tracers of incomplete combustion, exhibited obvious bimodal profiles at the CJZ and NQ sites, whereas those at the JYS site exhibited little change throughout the day. Isoprene at the three sites followed a similar pattern of average diurnal variations in mixing ratios, with minimums before sunrise and maximums at noon. The dominant sources of acetaldehyde and acetone were secondary anthropogenic source and aged air mass transport, respectively, in the city of Chongqing. Seven sources were apportioned to the results of PMF calculation using spatiotemporal VOCs composition data. The Vehicle-related sources were the largest contributor at CJZ and NQ, contributing 44% and 37% of the total VOC mixing ratios, respectively, and exhibited clear diurnal variations. Aged background air, with 68% of total VOC emissions, dominated the VOC emissions at JYS. Solvent utilization was a very important contributor at NQ and coincided with the higher levels of aromatics. O 3 formation was generally VOC-limited at NQ and CJZ, and was NO x -limited and transition region alternatively at JYS. Alkenes were important for the O 3 formation at CJZ, and both alkenes and aromatics were important for the O 3 formation at NQ. [ABSTRACT FROM AUTHOR]

With the objective of reducing the large uncertainties in the estimations of emissions from crop residue open burning, an improved method for establishing emission inventories of crop residue open burning at a high spatial resolution of 0.25° × 0.25° and a temporal resolution of 1 month was established based on the moderate resolution imaging spectroradiometer (MODIS) Thermal Anomalies/Fire Daily Level3 Global Product (MOD/MYD14A1). Agriculture mechanization ratios and regional crop-specific grain-to-straw ratios were introduced to improve the accuracy of related activity data. Locally observed emission factors were used to calculate the primary pollutant emissions. MODIS satellite data were modified by combining them with county-level agricultural statistical data, which reduced the influence of missing fire counts caused by their small size and cloud cover. The annual emissions of CO 2 , CO, CH 4 , nonmethane volatile organic compounds (NMVOCs), N 2 O, NO x , NH 3 , SO 2 , fine particles (PM 2.5 ), organic carbon (OC), and black carbon (BC) were 150.40, 6.70, 0.51, 0.88, 0.01, 0.13, 0.07, 0.43, 1.09, 0.34, and 0.06 Tg, respectively, in 2012. Crop residue open burning emissions displayed typical seasonal and spatial variation. The highest emission regions were the Yellow-Huai River and Yangtse-Huai River areas, and the monthly emissions were highest in June (37%). Uncertainties in the emission estimates, measured as 95% confidence intervals, range from a low of within ± 126% for N 2 O to a high of within ± 169% for NH 3 . [ABSTRACT FROM AUTHOR]

Monitoring dust aerosol properties is critical for the studies of radiative transfer budget, climate change, and air quality. Aerosol optical thickness (AOT) and effective radius (R eff) are two main parameters describing the optical and microphysical properties of airborne dust aerosol. Satellite remote sensing provides an opportunity for estimating the two parameters in spatial coverage and continuously. To take the merits of machine learning algorithms and also utilize the physical knowledge discovered in the conventional retrieval algorithms, a physical-based machine learning method was proposed and applied on the Himawari-8 geostationary satellite for robust retrieval of dust aerosol properties. The main concepts of this study comprise i) constructing the model input data by extracting highly informative features from the Himawari observations according to physical knowledge and ii) exploiting the utility of six state-of-the-art machine learning algorithms in dust aerosol retrieval. The algorithms include artificial neural network (ANN), extreme boost gradient tree (XGBoost), extra tree (ET), random forest (RF), support vector regression (SVR), and kernel Ridge regression (Ridge). The ground-truth AOT and R eff data from AERONET stations were supplied as output labels. The cross-validation technique was adopted for model training and the results show that the ANN model is superior to the other machine learning models for both AOT and R eff estimation, which exhibits the lowest mean absolute error (MAE = 0.0292 and 0.0981) and the highest correlation coefficient (r = 0.98 and 0.84). When validated on an independent dataset, the ANN model achieved the lowest MAE (0.0334 and 0.1487), and the highest r (0.94 and 0.63). More importantly, when compared against representative physical-based algorithms, the developed ANN model still retains the best performance. Furthermore, the ANN model shows an overall better performance than other machine learning models and also the JAXA Himawari-8 Level-2 AOT product, with examples exhibited in three dust storm events and for continuous monitoring of one of the dust storm events. Additionally, feature importance analysis implies that the important features of dust aerosol identified by the ANN model are consistent with that in physical model-based algorithms. In summary, this study shows great potential for generating near-real-time products of dust aerosol properties from Himawari satellite data. These products can provide a scientific basis for climate and meteorological study regarding severe dust storms. • Dust optical thickness and effective radius are retrieved simultaneously from Himawari-8 data. • Physical knowledge about dust aerosols is integrated with the machine learning models. • The ANN outperforms the XGBoost, extra tree, random forest, SVR, and Ridge regression models. • The ANN model is superior to the official aerosol retrieval algorithm of Himawari-8. • Near-real-time estimation of dust aerosol properties is achievable. [ABSTRACT FROM AUTHOR]

VOCs play an increasingly important role in affecting air quality and threatening human health in China in recent years, where industry activities show a significant contribution to VOCs emission. In this article we report our long term study of industrial VOCs emissions of six major industries (vehicle manufacturing, printing, equipment coating, electronic manufacturing, furniture manufacturing and bio-pharming) on the aspects of emission characteristics, environmental impact and health risk assessment, and control challenge analysis with the purpose to obtain in-depth understanding of industry VOCs emissions and offer some original basements for national control and management of industry VOCs emissions. This study shows that all these industries give middle or low emission with total VOCs concentration less than 1000 mg/m at each exhaust pipe. Benzenes, esters, alcohols, ketones, alkanes, chloroalkanes and alkenes were detected as the major emission components and the most frequently monitored VOCs were benzenes, which varied obviously with different processes and industries. The environmental impact assessments indicate that vehicle manufacturing and benzenes should be prior controlled with the purpose to reduce air pollution. While, health risk assessments suggest that furniture manufacturing and chloroalkanes should be firstly controlled. Control analysis indicates that developing technologies with low cost and high efficiency and establishing and completing specific industry emission standards/regulations are the two key issues in VOCs emission management at present stage. [ABSTRACT FROM AUTHOR]

The abatement of NO with propane in the presence of excess oxygen has been investigated on several kinds of commercial USY (Ultra Stable Y zeolite) in the Na-form (Na-USY) and H-form (H-USY) or wet impregnated to various extents with Ni ions (Ni/USY) catalysts. Experimental results indicated that Ni/H-USY loaded with 15% Ni showed comparatively better catalytic activity for Selective Catalytic Reduction (SCR) of NO with propane and it can suppress effectively the formation of N2O and CO. The reaction order was also studied and the reactants effect was discussed on the catalyst of Ni/USY. The new catalyst samples were characterised by measurements of BET, X-Ray Diffraction (XRD), H2-Temperature Programmed Reduction (TPR), Scanning Electron Microscopy (SEM) and IR for pyridine adsorption and NO adsorption. The chemical analysis indicates that NiO is distributed on the external surface of the zeolite particles. A synergistic effect between Ni2+ and Bronsted acids is responsible for the SCR of NO on Ni/H-USY. Nitrate compounds of Ni2+ formed on Ni/USY after NO adsorption favour the reduction of NO with propane. [ABSTRACT FROM AUTHOR]

The Chinese government has implemented a number of environmental policies to promote the continuous improvement of air quality while considering economic development. Scientific assessment of the impact of environmental policies on the relationship between air pollution and economic growth can provide a scientific basis for promoting the coordinated development of these two factors. This paper uses the Tapio decoupling theory to analyze the relationship between regional economic growth and air pollution in key regions of air pollution control in China—namely, the Beijing–Tianjin–Hebei region and surrounding areas (BTHS), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD)—based on data of GDP and the concentrations of SO2, PM10, and NO2 for 31 provinces in China from 2000 to 2019. The results show that the SO2, PM10, and NO2 pollution in the key regions show strong and weak decoupling. The findings additionally indicate that government policies have played a significant role in improving the decoupling between air pollution and economic development. The decoupling between economic growth and SO2 and PM10 pollution in the BTHS, YRD, and PRD is better than that in other regions, while the decoupling between economic growth and NO2 pollution has not improved significantly in these regions. To improve the relationship between economic growth and air pollution, we suggest that the governments of China and other developing countries should further optimize and adjust the structure of industry, energy, and transportation; apply more stringent targets and measures in areas of serious air pollution; and strengthen mobile vehicle pollution control. [ABSTRACT FROM AUTHOR]

The association between PM 2.5 (particulate matter ≤ 2.5 µm) short-term exposure and its health effect is non-linear from the epidemiological studies. And this nonlinearity is suggested to be related with the PM 2.5 heterogeneity, however, the underlying biological mechanism is still unclear. Here, a total of 38 PM 2.5 filters were collected continuously for three weeks in winter Beijing, with the ambient PM 2.5 varying between 10 and 270 µg/m3. Human monocytes-derived macrophages (THP-1) were treated with PM 2.5 water-soluble elutes at 10 µg/mL to investigate the PM 2.5 short-term exposure effect from a proinflammatory perspective. The proinflammatory cytokine tumor necrosis factor (TNF) induced by the PM 2.5 elutes at equal concentrations were unequal, showing the heterogeneity of PM 2.5 proinflammatory potentials. Of the various chemical and biological components, lipopolysaccharide (LPS) showed a strong positive association with the TNF heterogeneity. However, some outliers were observed among the TNF-LPS association. Specifically, for PM 2.5 from relatively clean air episodes, the higher LPS amount corresponded to relatively low TNF levels. And this phenomenon was also observed in the promotion tests by treating macrophages with PM 2.5 elutes dosed with additional trace LPS. Gene expression analysis indicated the involvement of oxidative-stress related genes in the LPS signaling pathway. Therefore, a potential oxidative-stress-mediated suppression on the PM 2.5 -borne LPS proinflammatory effect was proposed to be accounted for the outliers. Overall, the results showed the differential role of LPS in the heterogeneity of PM 2.5 proinflammatory effects from a component-based perspective. Future experimental studies are needed to elucidate the signaling pathway of LPS attached on PM 2.5 from different air quality episodes. [Display omitted] [ABSTRACT FROM AUTHOR]

Biomass burning (BB) seriously affect air pollution, human health and global climate. A severe pollution episode (PE) caused by BB was investigated in the southern Sichuan Basin (SSB), one of the most polluted areas in China. Hourly variations in criteria air pollutants (PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3), chemical components, and sources of PM 2.5 before, during, and after the severe regional air PE were characterized at three sites, namely Neijiang (NJ), Zigong (ZG), and Yibin (YB). The results showed that combination of intensive pollution from BB, stable meteorological conditions, and the basin topography caused this severe regional PE in the SSB. The average daily concentrations of PM 2.5 during the PE were 1.8–6 times those measured during the periods before and after the PE, and 4.0–7.4 times that of World Health Organization air quality guidelines in the SSB. The highest PM levels occurred in ZG, where the peak values of PM 2.5 and PM 10 reached 536 μg m−3 and 578 μg m−3 at night, respectively. PM 10 , NO 2 , and CO also increased dramatically at night in the SSB. O 3 formation was affected by BB, showing lower levels at night but higher levels in the day during the PE than before and after the PE, whereas SO 2 levels were not affected. Sulfate–nitrate–ammonium in PM 2.5 was the main chemical compositions before the PE, whereas organic matter (OM) and K+ became characteristics compositions during and after the PE. Higher OC/EC and K excess /EC ratios were observed during the PE and K excess /EC ratio was a better indicator of BB in the SSB than OC/EC ratio. The results of a positive matrix factorization model indicated that BB was the most significant contributor to PM 2.5 during the PE, accounting for 58% in NJ, 65% in ZG, and 56% in YB. Backward trajectory analysis confirmed that the SSB is susceptible to pollutants from Chongqing and other surrounding cities, especially in ZG and NJ, due to the unique topography of the basin. Our findings suggest that BB in the basin topography can cause severe regional air pollution events at night, thus supporting the critical need for BB control in the basin to improve regional air quality. Image 1 • The evolution of region air pollution event caused by biomass burning was observed. • PM 2.5 , PM 10 , NO 2 , and CO increased dramatically during the pollution episode. • O 3 formation was influenced by biomass burning. • Chemical components and sources variations of PM 2.5 were investigated. • The southern Sichuan Basin was susceptible to air masses from Chongqing. [ABSTRACT FROM AUTHOR]

It is critically important to include the co-benefits of abated air pollution when sharing carbon mitigation efforts among provinces. Therefore, using a Chinese multi-regional computable general equilibrium model, this study incorporated the pollutant co-benefits into the carbon marginal abatement cost curves and evaluated the inter-provincial abatement effort sharing for China's provinces to achieve the Nationally Determined Contribution target. Results show that the more developed eastern economies face higher abatement costs under the same abatement level compared to the less developed central and western provinces. Second, in the composition of total co-benefits among provinces, the co-benefits of SO 2 reductions exceed 60% followed by the co-benefits of NO X and PM 2.5 reductions. Finally, the provincial abatement costs will be offset by 4.3% to 18.9% after considering the co-benefits. Specifically, provinces with high per capita GDP and energy-intensive industries (e.g. Shandong, Liaoning, and Jilin) and some provinces with energy production bases (e.g. Inner Mongolia, Shanxi, and Xinjiang) have higher co-benefits and offset the more abatement costs; therefore, they can consider raising abatement efforts. Moreover, provinces with high economic levels but fewer co-benefits (e.g. Beijing, Tianjin, and Shanghai) can consider providing climate funding or transferring abatement technologies to support the abatement work of less developed provinces. • A cost-benefit analysis for China's provincial NDC target considering long-ignored co-benefits. • Employ MACCs of multiple pollutants to quantify the co-benefits of carbon abatement actions. • Original carbon marginal abatement cost curves can be offset with air-quality co-benefits. • We compensated for the lack of the general equilibrium effects in bottom-up models. [ABSTRACT FROM AUTHOR]

• This study is the first to investigate indoor formaldehyde contribution to ambient. • Residential building materials are a significant source of ambient formaldehyde. • The share of residential emissions to 81 anthropogenic sources was 6.66% (rank 6th). • Residential emission intensities were higher in more urbanized areas. • Building density, building materials, T and RH are influencing factors of emission. This study investigates the contribution of formaldehyde from residential building materials to ambient air in mainland China. Based on 265 indoor field tests in 9 provinces, we estimate that indoor residential sources are responsible for 6.66% of the total anthropogenic formaldehyde in China's ambient air (range for 31 provinces: 1.88–18.79%). Residential building materials rank 6th among 81 anthropogenic sources (range: 2nd–10th for 31 provinces). Emission intensities show large spatial variability between and within regions due to different residential densities, emission characteristics of building materials, and indoor thermal conditions. Our findings indicate that formaldehyde from the indoor environment is a significant source of ambient formaldehyde, especially in urban areas. This study will help to more accurately evaluate exposure to ambient formaldehyde and its related pollutants, and will assist in formulating policies to protect air quality and public health. [ABSTRACT FROM AUTHOR]