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Photochemical air quality models (AQMs) are a vital tool for atmospheric pollution research and have been widely used in various applications, such as air quality prediction and evaluation of pollution control strategies. Before using these models for further studies, it is essential to thoroughly evaluate their reliability and accuracy. While previous guidelines and benchmarks have primarily focused on fine particulate matter (PM 2.5) and ozone (O 3), there is still a lack of benchmarks for evaluating the model performance on primary criteria pollutants such as sulfur dioxide (SO 2), nitrogen dioxide (NO 2), carbon monoxide (CO), and particulate matter with aerodynamic diameter less than or equal to 10 μm (PM 10). The use of air quality models in China has increased significantly in the past decades. However, there is still a lack of standardized benchmarks for the performance evaluation of these models. Building upon our previous work on PM 2.5 and its chemical species, we propose a set of benchmarks for evaluating the performance of the aforementioned four air pollutants. Initially, we identified a total of 475 papers published during 2007–2019 that utilized at least one of the five commonly used AQMs in China. From these papers, we selected 164 articles that provided model performance evaluation (MPE) results of the four primary air pollutants. The three most frequently used Model Performance Evaluation (MPE) metrics were selected to analyse the impact of different model configurations on the reported statistics, including modelling region, season, and emission inventory. Lastly, three commonly used statistical indicators, including normalized mean bias (NMB), normalized mean error (NME), and correlation coefficient (R), were proposed for the validation of simulated NO 2 , SO 2 , CO, and PM 10. Two sets of benchmarks are given, including the "goal" and "criteria". The "goal" represents the best range of performance that a model can be expected to achieve, and the "criteria" represents performance that the majority of studies have achieved. We recommend R values above 0.50, 0.35, 0.45, and 0.40 for NO 2 , SO 2 , PM 10 , and CO, respectively, in order to meet the "criteria" benchmark. If the "goal" benchmark is to be achieved, the corresponding R values are 0.60, 0.55, 0.60, and 0.60. The "goal" benchmarks of NMB for NO 2 , SO 2 , PM 10 , and CO are within ±20%, ±25%, ±20%, and ±25%, respectively; while the "goal" benchmarks of NME for the four pollutants are less than 40%, 45%, 45%, and 60%, respectively. These benchmarks supplement our previous benchmarks for PM 2.5 and its components and provide a more comprehensive guideline for the air quality modelling community in China. • A set of benchmarks for evaluation of the NO 2 , SO 2 , CO and PM 10 simulation over China are proposed. • Three commonly used statistical indicators (NMB, NME, and R) were proposed for the validation of the four pollutants. • The goal benchmarks of NMB for NO 2 , SO 2 , PM 10 , and CO are <20%, <25%, <20% and <25%, respectively. [ABSTRACT FROM AUTHOR]

COVID-19 can be considered as the largest public health incident since 2019, posing disturbances to the medical, economic, and social systems. Understanding different levels of city resilience to the impact of COVID-19 on sustainable urban development is therefore essential. In this paper, we analyzed the evolution of the pandemic, the recovery of urban activities and major drivers of urban resilience to COVID-19 based on urban activity patterns derived from NO 2 monitoring stations from 217 cities in China. It is observed that nearly all urban activities have been affected by the epidemic with a reduced NO 2 emission, indicating a significant decline in the intensity of urban activity. The recovery patterns of human activity among different cities show that: (1) northern cities where low-resilience cities agglomerated have been severely affected. As of April 31, 2020, 12 northern cities have not recovered to pre-epidemic levels; (2) about three-quarters of the cities went through multiple stages to return to the pre-pandemic level, which related to both pandemic stress and prevention measures; (3) about 46.04% of the cities experienced increasing activity patterns that exceeded the pre-epidemic activity level. It is also observed that urban resilience can behave quite differently driven by variations in green coverage, economic aggregate, employment security, and medical system. Our study highlights the potential of an elevated urban resilience by optimizing the urban layout, improving the quantity and quality of green space, and enhancing the medical system. [Display omitted] • Most of the southern cities of China show higher urban activity than pre-pandemic. • Low/high-resilient urban agglomerations located in the north-northeast/south China. • Most cities undergo two stages of recovery at the beginning and end of March 2020. • Urban greenery is positively correlated to urban resilience. • Hospital capacity, instead of number, is more crucial to urban resilience. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the surface solar radiation data from 1960 to 2000 gathered from 40 weather stations over China were reexamined, and the relationship of long-term trends of the solar radiation and climate factors were analyzed. The results indicate that the surface solar radiation in most regions of China begins to increase after 1990. Decreases in cirrus and cirrostratus clouds, which account for a larger percentage of the total cloud amount over China, have an important contribution to the increasing trend of the surface solar radiation. Further examination of the surface water vapor changes reveals that the surface solar radiation negatively correlates with the near surface water vapor in most region of China, and this negative correlation is more pronounced in higher latitudes of China where the atmosphere is compared to regions in southern China. [Copyright &y& Elsevier]

In a publication in Atmospheric Environment ( http://dx.doi.org/10.1016/j.atmosenv.2015.10.081 ), Gu et al. (2015) estimated that “the total nitrogen (N) deposition in 2010 was 2.32 g N m −2 yr −1 ” in China. This value is comparable with previous estimations based on a synthesized dataset of wet/bulk inorganic N deposition observations, which underestimates the total N deposition since their algorithm (equations (2) and (3) in their paper) does not account for dry deposition of NH 3 , HNO 3 , NO x and wet/dry deposition of HONO and organic nitrogen (e.g. amines, amides, PAN). Indeed, Gu et al. (2015) mixed the terminology of wet/bulk deposition and total deposition. Another flawed assumption by Gu et al. (2015) is that all inorganic N in precipitation estimated by their algorithm originates from fertilizer and coal combustion. This is incorrect and almost certainly causes biases in the spatial and temporal distribution of estimated wet/bulk inorganic N deposition (Fig. 5 in their paper), further considering the fact that they neglected important N sources like livestock and they did not consider the nonlinearity between various sources and deposition. Besides the input data on N deposition, the model validation (Sect. 2.3.2) described in their paper also requires clarification because the detailed validation information about the time series of observational dataset versus modeling results was not given. As a result of these combined uncertainties in their estimation of N deposition and the lack of detail for model-measurement comparison, their estimates of the impacts of N deposition on carbon storage in Chinese forests may need further improvement. We suggest the clarification of the terminology regarding N deposition, especially for wet deposition, bulk deposition, gaseous and particulate dry deposition or total deposition since the accurate distinction between these terms is crucial to investigating and estimating the effects of N deposition on ecosystems. [ABSTRACT FROM AUTHOR]

In the second of two companion papers, we quantify the potential ultimate benefits for public health and radiative forcing from the phase-out of coal use in China. With the baseline year of 2015, we estimate that the premature deaths and disability-adjusted life years (DALYs) in China will be reduced by 375,000 persons and 8.90 × 106 years (GEMM model), which is 41.7% and 54.5% of the total number of premature deaths and DALYs caused by PM 2.5 from anthropogenic sources, effectively improving the health of the population. The provinces with the most significant decreases in fatalities and disabilities are concentrated in large coal-consuming provinces. Regarding age distribution, the phase-out of coal use will effectively extend the life expectancy of the elderly, especially in the age group of 65–89 years. Health benefits' growth rate upsurges notably once more than half of coal is phased out. Based on the Parallel Offline Radiative Transfer (PORT) model, the global annual average radiative forcing due to changes in short-lived climate pollutants (SLCPs), including sulfate, black carbon, organic carbon, and O 3 associated with the phase-out of coal use, is 0.34 W m−2, -0.048 W m−2, 0.00064 W m−2, and 0.00055 W m−2, respectively. The complete phase-out of coal use in China also reduces the emission of long-lived climate pollutants (LLCPs) and causes negative radiative forcing. Among them, the global annual average radiative forcing of CO 2 , CH 4 , and N 2 O is −0.0073 W m−2, -5.48 × 10−5 W m−2, -1.60 × 10−5 W m−2. If the lifetimes of pollutants are considered, the total global reduction in heat uptake is −2.29 × 107 J m−2 over 100 years, dominated by the decrease of CO 2 , which will slow the rate of global warming and thus may reduce the probability of extreme weather events. This report will incentivize relevant policymakers to remove coal significantly to achieve carbon neutrality. [Display omitted] • Coal phase-out in China avoids half of premature deaths and DALYs attributed to PM 2.5 emission from anthropogenic sources. • The health benefits experiences an upsurge when coal removal exceeds 50%, non-linearly related to the coal removal. • Sulfate and CO 2 dominate positive and negative radiative forcing. Considering the lifetime, a cooling effect will occur. [ABSTRACT FROM AUTHOR]

As an economically intensive city within the greater bay area (GBA), Shenzhen has suffered from severe ozone pollution in recent years. Numerical modeling is an important tool in daily air quality prediction. However, a combination of factors, such as outdated emission inventories and the uncertainty of weather and numeric models, pose challenges for accurately forecasting pollution periods and levels. Therefore, systematic evaluation of the numerical model performance is crucial for better manual revised prediction of the daily MAD8h and air quality index (AQI). In this paper, a multimodel air quality forecast system, which was built in our public-owned monitoring center station, was evaluated to understand the possibilities of improvements. Based on the 2021 observed data of the national air monitoring station, we utilized a series of metrics to access the predicted air quality index (AQI) and pollution concentration levels from models (including WRF-CMAQ, NAQPMS, and WRF-CHEM) and compared them to the manual prediction. The results indicated that WRF-CMAQ performs outstandingly in comprehensive analysis, with a yearly 24 h (hour) forecast accuracy (A) exceeding 91%. The NAQPMS 24 h forecast presented the lowest accuracy (A = 71%) with a high overprediction rate. The forecast accuracy of WRF-CHEM and NAQPMS in the winter period was 100%. However, frequently changing weather patterns interfered with spring forecasting. Forecasters can accurately predict mild pollution cases (A = 81.8% for 24 h, A = 72.7% for 48 h), whereas numerical models (NAQPMS and WRF-CHEM) showed better sensitivity to moderate pollution cases. For fine particulate evaluation, CMAQ's low biases occurred in winter, and the manually revised air quality forecast (MRAQR) normally gave higher prediction values except in pollution cases. Model underestimation could be explained by missing the long-range dust transport contribution from the northern part of China. The ozone 24 h forecast part revealed that WRF-CMAQ exhibited the highest correlation coefficient (R = 0.70, 19.74% < RMSE < 42.7%), whereas NAQPMS showed relatively poor results with the highest ME (43 μg/m3) and MFE (41.92%). Close examination of synoptic information revealed that ozone pollution cases were subjected to the western Pacific subtropical high (WPSH) system, tropical cycle, and regional strong pollution transport. The NAQPMS ozone overprediction could be explained by the overestimation of troposphere transmission from the surroundings during nonconductive weather patterns. The study investigates a theoretical method for model optimization, which provides solid support for precise emission reduction and targeted pollution control. • Established a method for evaluating operational air quality forecasting. • The performance in predicting mild pollution cases in the GBA is slightly weak. • The transportation process is the main source of current prediction bias. [ABSTRACT FROM AUTHOR]

Crop residue burning is an important source of air pollutants and strongly affects the regional air quality and global climate change. This study presents a detailed emission inventory of major air pollutants from crop residue burning for the year of 2014 in China by the bottom-up method. Activity data were investigated for 296 prefecture-level cities. Emission factors were determined for indoor and in-field burning separately. Regional differences were considered for the proportion of residue burned (PCRB), the ratio between indoor and in-field burning, and the ratio of straw to grain production. The emissions were estimated at prefecture-city level as the first step; then they were redistributed within a city based on 1-km resolution land-use, MODIS fire counts, and rural population. Temporal variation was determined according to farming practices in different regions and MODIS fire counts. Uncertainties were estimated using the Monte Carlo method. The total emissions from crop residue burning in China were estimated to be 0.13 (−47–92%) for BC, 0.71 (−48–92%) for OC, 1.77 (−48–91%) for PM 2.5 , 2.04 (−50–100%) for PM 10 , 0.16 (−59–133%) for SO 2 , 0.53 (−55–105%) for NO X , 0.12 (−47–93%) for NH 3 , 1.07 (−55–102%) for CH 4 , 1.85 (−43–74%) for NMVOC, 18.33 (−46–85%) for CO and 305.20 (−45–80%) for CO 2 , in unit of Tg yr−1. Our results are remarkably lower than those reported in previous studies, mainly because of the PCRB has decreased significantly in recent years. For most of the pollutants, indoor burning accounted for about 50–70% of the emissions. Rice, wheat and corn contributed more than 85% of the emissions, but their relative contributions varied a lot with region and season. High emissions were mostly located in the eastern China, central China and northeastern China, and temporally peaked in April, June and October, with different intensities in the north and the south. This study provides a useful basis for air quality modeling and the policy making of pollution control strategies. • This paper presents an up-to-date and high-resolution inventory of air pollutant emissions from crop residue burning in China. • City-level activity data and high-resolution land use, fire counts and population were combined for spatial distribution. • Farming practices in different regions and statistics of fire counts were used to determine temporal distribution. • High emissions were generally located in Eastern, Central and Northeastern China, and temporally peaking in April, June and October. [ABSTRACT FROM AUTHOR]

Sixty rainwater samples were collected covering four seasons from July 2020 to April 2021 from the center of Chongqing, a metropolitan city of southwest China, using automatic deposition sampler. The samples were analysed for 17 targeted poly- and per-fluoroalkyl substances (PFASs) by HPLC-MS/MS. The concentration of PFASs ranged from 3.31 ng/L to 196.14 ng/L and dominated by perfluoro-n-octanoic acid (PFOA), perfluoro-n-hexanoic acid (PFHxA), perfluoro-n-butanoic acid (PFBA) and sodium perfluoro-1-octanesulfonate (PFOS). The seasonal variations of dissolved and particulate PFAS were distinct, with the highest level in summer and autumn, respectively. Positive matrix factorization (PMF) and a wet deposition model were performed to apportion the sources and estimate the wet deposition flux of the 17 PFASs, respectively. PMF analysis indicated that, based on yearly average, paper packaging production (37.4%) and aqueous film forming foam (AFFF) usage (29.8%) were the two major sources, followed by textile production (16.7%) and electronic product manufacturing (16.1%). The highest source contributor for PFASs in winter was AFFF usage (47.0%); while in the other three seasons, it was paper packaging production that contributed the most (50.1%, 39.8% and 34.8% respectively). Seasonal average wet deposition flux of PFASs was estimated to be 13.9 ng/m2/day. The dry deposition of PFASs was estimated to account for 15.8% of the total atmospheric deposition flux, suggesting a more important role for wet deposition. The results of this study provide important information for understanding of PFASs occurrence and atmospheric wet deposition in Chongqing, and other urban centers across China. [Display omitted] • The dissolved and particulate PFASs in rainwater was 20.3 ng/L and 4.39 ng/L, respectively. • PMF identified paper packaging production was the major sources. • The average wet deposition flux of PFASs was estimated as 13.9 ng/m2/day. [ABSTRACT FROM AUTHOR]

Abstract: This paper, which focuses on diesel trucks, is the third in a series of three papers published in Atmospheric Environment to understand vehicle emissions in China by conducting on-board emission measurements. Diesel trucks are a significant source of emissions in ambient air, especially for NOx. Recently, China announces an aggressive target to reduce national NOx emissions by 10% from 2010 to 2015 in the “Twelfth Five-Year Plan (2011–2015)” and diesel vehicles are identified as a key target for NOx control. However, the understanding of the real-world emissions of diesel trucks is limited. In this study, we measured HC, CO, NOx, and PM2.5 emissions from 175 diesel trucks of different sizes and technologies in five Chinese cities during 2007 and 2011, and generated emission factors on the basis of the measurements. The results show that the HC, CO, and PM2.5 emission factors have been reduced significantly as the emission standards become more stringent from Euro 0 to Euro IV, but the NOx emission factors change differently. Euro II trucks have 3–6% higher NOx emission levels than Euro I technologies and Euro III trucks fail to show a reduction as regulated by the standards. More stringent NOx requirements (e.g. Euro IV) for diesel vehicles need to be enforced. The comparison with the emission factors used in recent emission inventory studies shows that these inventories may have overestimated or underestimated diesel emissions for the years after 2006. This study emphasizes the importance of conducting local measurement research to improve the accuracy of the estimates of mobile emissions in China. [Copyright &y& Elsevier]

Abstract: This paper is the second in a series of three papers aimed at understanding the emissions of vehicles in China by conducting on-board emission measurements. This paper focuses on light-duty gasoline vehicles. In this study, we measured 57 light-duty gasoline vehicles (LDGVs) in three Chinese mega-cites (Beijing, Guangzhou, and Shenzhen), covering Euro 0 through Euro IV technologies, and generated CO, HC, and NOx emission factors and deterioration rates for each vehicle technology. The results show that the vehicle emission standards have played a significant role in reducing vehicle emission levels in China. The vehicle emission factors are reduced by 47–81%, 53–64%, 46–71%, and 78–82% for each phase from Euro I to Euro IV. Euro 0 vehicles have a considerably high emission level, which is hundreds of times larger than that of Euro IV vehicles. Three old taxis and four other Euro I and Euro II LDGVs are also identified as super emitters with equivalent emission levels to Euro 0 vehicles. Of the measured fleet, 23% super emitters were estimated to contribute 50–80% to total emissions. Besides vehicle emission standards, measures for restricting super emitters are equally important to reduce vehicle emissions. This study is intended to improve the understanding of the vehicle emission levels in China, but some key issues such as emission deterioration rates are yet to be addressed with the presence of a sufficient amount of vehicle emission measurements. [Copyright &y& Elsevier]

Abstract At present, there were countless documents and periodicals which have been paying more attention to the impact factors of carbon emissions at different levels. Nevertheless, in previous studies, regional classification methods are always based on geographical location or single factor which lack of comprehensive indicators for measurement. In this paper, 30 provinces are divided into three areas (advantageous area, potential area and backward area) from 1996 to 2015 by factor analysis and cluster analysis according to the different social development which is measured by urbanization, economy, energy utilization, industry and technology. An extented STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model containing with panel regression models are utilized to evaluate the influence of factors on CO 2 emissions in China at national and diverse regional levels. The results indicate that the R&D investment and energy cleanliness play important roles in reducing CO 2 emissions at advantageous area. Besides, the impact of demographic factor in potential area is higher than the other two areas. Meanwhile, improving the tertiary industry level is more essential in backward area than others. On the whole, the effect of economic growth has a greatest positive influence among three regions. Nevertheless, there exists EKC curve for three regions which shows a downward trend of CO 2 emissions with increasing GDP per capita in the future. Finally, the concrete solution depends, of course, on the circumstances in three differentiated regions. Graphical abstract Image 1 Highlights • The existence of EKC hypothesis for carbon emissions is tested for China in national and regional level. • The extend STIRPAT model to examine the impact factors of CO 2 emissions. • The SYS-GMM model and FE-DK model are employed to fit the extended STIRPAT model. • Economic growth is the most important impact factors of CO2 emissions. [ABSTRACT FROM AUTHOR]

Abstract Multiangle Implementation of Atmospheric Correction (MAIAC) is a new aerosol algorithm developed to retrieve aerosol optical depth (AOD) over land using the time series data to dynamically isolate aerosol and land contributions. However, there are still no comprehensive research on the quality of MAIAC AOD over China. In this paper, 1 km MAIAC AOD over China were examined against ground-based measurements to evaluate the performance of the data. In general, Aqua and Terra MAIAC retrievals have a high correlation coefficient (0.924 for Aqua and 0.933 for Terra) with ground-based observations and there are more than 72% of retrievals falling within the Expected Errors (EE = ±(0.05 + 0.2*AOD)). We found that the accuracy of MAIAC AOD is related with the AOD magnitude, aerosol size, seasons and surface types. In spring and summer, the AOD bias was influenced by both aerosol model and surface reflectance estimation. We also found that the aerosol model is the main source of AOD bias over desert regions. These results indicated that the MAIAC AOD could be used as a new aerosol data source for air quality and climate studies in China. Highlights • A comprehensive research was conducted to evaluate the performance of MAIAC aerosol products over China. • The accuracy of MAIAC AOD is related with the AOD magnitude, aerosol size, seasons, and surface types. • Good performance of Aqua and Terra MAIAC retrievals are shown. • The MAIAC AOD bias is mainly influenced by aerosol model in desert regions. [ABSTRACT FROM AUTHOR]

Abstract: By means of computational fluid dynamic (CFD) simulations, this paper investigated how rural winds transport relatively clean air into high-rise urban areas to dilute airborne pollutants. Two ventilation concepts for indoor environments were applied: the age of air to quantify the time taken by rural young air to reach a given place after it enters an urban area and the air exchange efficiency to evaluate the efficiency of rural winds flushing the entire urban canopy layer. Several square building arrays with street aspect ratios (building height/street width, H/W) ranging from 1.5 to 5.3 and building area densities of 0.25 (medium) or 0.4 (compact) were considered as the approaching wind is parallel to the main street whose length varies from street scales to neighborhood scales (330–510 m to 1.03–1.65 km in full-scale). Results show that considerable young rural air enters windward entries but a major fraction of air is vertically driven out as flowing deeper into such high-rise building arrays. So air exchange efficiencies are less than 50% in street-scale arrays, and smaller in longer (neighborhood-scale) or narrower arrays. For the neighborhood-scale medium arrays, considering the power-law velocity profile in the upstream free flow, a taller array gains a larger inflow rate across its windward entry and experiences younger air and greater air exchange efficiency than a lower one. If all buildings are theoretically open-based in a neighborhood-scale compact array, air becomes much younger everywhere and the air exchange efficiency doubles. In arrays of buildings with different heights, the secondary streets in front of taller buildings get younger air due to the downward flows within them. Although further investigations are still required before providing a practical framework, this paper is one of the first attempts to find ways in improving the ventilation performance in high-rise cities like Hong Kong. [Copyright &y& Elsevier]

Abstract: This paper investigates numerical method for predicting wind field and pollutant dispersion in an urban area with crowded buildings and heavy traffic. Large eddy simulation is employed in numerical computation with Lagrangian dynamic model to close the subgrid scale stress. The fine grids are required in numerical simulation of micro-atmospheric flows in an urban area with crowded and complex building-streets. To realize the numerical simulation on available computer resources a domain decomposition method and a combined model for the building-street layout are proposed that the computed domain of an urban area is decomposed into a central area, where the pollution is the major concern and a surrounding region where the pollutant distribution is not concerned. The combined flow model is proposed for the complex building-street layout that the fine grid mesh and exact flow boundary conditions are prescribed in the central area while the buildings are treated as roughness elements in the surrounding region where the coarse grids are used. The proposed numerical method is applied to simulate the wind field and pollutant dispersion from vehicle exhaust in the Rua Do Campo area of Macau (referred to the site hereafter). The wind speed and temperature fields are computed from 7 am on September 6 2005 to 7 am of the next day and the pollutant concentration field is computed from 7 am to 6 pm on September 6 2005. The results show fairly good agreement with the field observation. The wind field and pollutant concentration distribution are demonstrated in the site by proposed combined model and they are compared with those computed by drag element model in whole computational domain. The results show that the combined model is an appropriate method for predicting wind field and pollutant dispersion in the crowded building area. The paper also reveals the relevance of buoyancy effect in the prediction of urban atmospheric environment. [Copyright &y& Elsevier]

Abstract: Carbon footprint has become a term often used by the media in recent days. The human carbon footprint is professed to be a very serious global threat and every nation is looking at the possible options to reduce it since its consequences are alarming. A carbon footprint is a measure of the impact of human activities on earth and in particular on the environment; more specifically it relates to climate change and to the total amount of greenhouse gases produced, measured in units of carbon dioxide emitted. Effort of individuals in minimizing the carbon footprint is vital to save our planet. This article reports a study of the carbon footprint of various types of shopping bags (plastic, paper, non-woven and woven) using life cycle impact assessment (LCIA) technique in two stages. The first stage (baseline study), comprised the study of the impact of different types of shopping bags in the manufacturing phase, without considering their usage and disposal phases (cradle to gate stage). The LCIA was accomplished by the IPCC 2007 method, developed by the Inter Panel on Climate Change in SIMAPRO 7.2. The GWP (Global Warming Potential) values calculated by the IPCC 2007 method for 100 years were considered as a directive to compare the carbon footprint made by the different types of shopping bags under consideration. The next stage was the study of the carbon footprint of these bags including their usage and disposal phases (cradle to grave stage) and the results derived were compared with the results derived from the baseline study, which is the major focus of this research work. The values for usage and end-of-life phases were obtained from the survey questionnaire performed amongst different user groups of shopping bags in China, Hong Kong and India. The results show that the impact of different types of shopping bags in terms of their carbon footprint potential is very high if no usage and disposal options were provided. When the carbon footprint values from different disposal options were compared, the carbon footprint values were lower in the case where a higher percentage of reuse is preferred to recycling and disposing to landfill. The results indicate that a higher percentage of reuse could significantly scale down the carbon footprint. Once the shopping bags reached the point where they can no longer be reused, they must be forwarded to recycling options, rather than being disposed to landfill. At this juncture, consumer’s perceptions and behaviours in connection with the respective government’s policies in promoting & facilitating recycling systems could be critical in reducing the carbon footprint of various shopping bags. [Copyright &y& Elsevier]

This paper has a dual focus. One is to study the spatial characteristics of dust activities over the Tarim Basin by locating dust active hot spots. The other is to study the temporal characteristics of dust activities over the Tarim Basin by applying the Extreme Value Analysis (EVA). Hot spots (in terms of dust loading) refer to relatively small (compared to the size of the whole source area), consistently active, dust producing areas. In this paper, Total Ozone Mapping Spectrometer (TOMS) Aerosol Index (AI) is used to identify hot spots in the Tarim Basin. This hot spot study suggests that dust emissions are inhomogeneous in the Tarim Basin area and the formation of the two hot spots is linked to both (1) a large amount of fine sandy sediments accumulated at the hot spot areas and (2) the basin''s internal dust transport pathways which are controlled by the extreme bounding topography of the basin. Extreme Value Analysis (EVA) has been widely used in a variety of areas, but has not yet been applied in dust storm research. In this paper, 13 years Tarim Basin regional daily maximum TOMS AI data time series are constructed. By examining the statistics of the time series, a new method (outlier method) is developed to identify extreme values. EVA is applied and 100-year-return values are suggested. The interpretation of the 100-year-return values is discussed by referring to historical meteorological records and also climate change projections. [Copyright &y& Elsevier]

Both meteorological changes and emission control have significant impacts on PM 2.5 concentration and regional transport contribution, where it is important to clarify the relationship among them for air pollution control. In this paper, the WRF-CAMx coupled with a set of simulation scenarios was employed for decomposing the contributions of meteorological conditions, emission, and regional transport considering transport matrix to PM 2.5 concentration in the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions during 2013–2017. The results show that the local contributions occupied the primary position in PM 2.5 concentrations in the BTH and YRD regions, reaching 57.9% and 58.1% in 2017, respectively. In terms of regional transports, they were mainly from Shandong Peninsula (SP) and Fen Nutrient Plain (FNP) for BTH, while Central triangle Region (CTR) and SP for YRD. Analysis of typical representative months suggested that in January, the concentration of local contribution reached peak. In addition, the results also show that when the intensity of emission reduction increased, the proportion of local contribution increased significantly while the local absolute contribution concentration decreased correspondingly. The meteorological conditions had little impact on transport matrix dominated by local contribution, whereas, the more favorable the meteorological diffusion conditions, the lower the contribution ratio of the out-of-region transport and the lower the contribution concentration. Moreover, the variation of meteorological conditions was more significant to the transport contribution outside the region. These findings can be helpful for formulating reasonable strategies of regional joint prevention and control and providing a valuable preference for other areas of China. [Display omitted] • The transport matrix of PM 2.5 was established using the WRF-CAMx model. • Emission reduction dominated the total decrease in PM 2.5. • Local emissions contributed 57.9% and 58.1% of PM 2.5 in BTH and YRD. • Meteorological conditions had a significant impact on the regional transport. [ABSTRACT FROM AUTHOR]

The PM 2.5 is the culprit of air pollution, and it leads to respiratory system disease when the fine particles are inhaled. Therefore, it is increasingly significant to develop an effective model for PM 2.5 forecasting and warnings that informs people to foresee the air quality. People can reduce outdoor activities and take preventive measures if they know the air quality is bad ahead of time. In addition, reliable forecasting results can remind the relevant departments to control and reduce pollutants discharge. According to our knowledge, the current hybrid forecasting techniques of PM 2.5 do not take the meteorological factors into consideration. Actually, meteorological factors affect the concentrations of air pollution, but it is unclear whether meteorological factors are helpful for improving the PM 2.5 forecasting results or not. This paper proposes a hybrid model called CEEMD-PSOGSA-SVR-GRNN, based on complementary ensemble empirical mode decomposition (CEEMD), particle swarm optimization and gravitational search algorithm (PSOGSA), support vector regression (SVR), generalized regression neural network (GRNN) and grey correlation analysis (GCA), for the daily PM 2.5 concentrations forecasting. The main steps of proposed model are described as follows: the original PM 2.5 data decomposition with CEEMD, optimal SVR selection with PSOGCA, meteorological factors selection with GCA, residual revision by GRNN and forecasting results analysis. Three cities (Chongqing, Harbin and Jinan) in China with different characteristics of climate, terrain and pollution sources are selected to verify the effectiveness of proposed model, and CEEMD-PSOGSA-SVR*, EEMD-PSOGSA-SVR, PSOGSA-SVR, CEEMD-PSO-SVR, CEEMD-GSA-SVR, CEEMD-GWO-SVR are considered to be compared models. The experimental results show that the hybrid CEEMD-PSOGSA-SVR-GRNN model outperforms other six compared models. Therefore, the proposed CEEMD-PSOGSA-SVR-GRNN model can be used to develop air quality forecasting and warnings. [ABSTRACT FROM AUTHOR]

As the world's largest coal producer and consumer, China accounts for a relatively high proportion of methane emissions from coal mines. Several estimation methods had been established for the coal mine methane (CMM) emission. However, with large regional differences, various reservoir formation types of coalbed methane (CBM) and due to the complicated geological conditions in China, these methods may be deficient or unsuitable for all the mining areas (e.g. Jiaozuo mining area). By combing the CMM emission characteristics and considering the actual situation of methane emissions from underground coal mine, we found that the methane pre-drainage is a crucial reason creating inaccurate evaluating results for most estimation methods. What makes it so essential is the extensive pre-drainage quantity and its irrelevance with annual coal production. Accordingly, the methane releases were divided into two categories: methane pre-drainage and methane release during mining. On this basis, a pioneering method for estimating CMM emissions was proposed. Taking the Yanma coal mine in the Jiaozuo mining area as a study case, the evaluation method of the pre-drainage methane quantity was established after the correlation analysis between the pre-drainage rate and time. Thereafter, the mining activity influence factor ( MAIF ) was first introduced to reflect the methane release from the coal and rock seams around where affected by mining activity, and the buried depth was adopted as the predictor of the estimation for future methane emissions. It was verified in the six coal mines of Jiaozuo coalfield (2011) that the new estimation method has the minimum errors of 12.11%, 9.23%, 5.77%, −5.20%, −8.75% and 4.92% respectively comparing with other methods. This paper gives a further insight and proposes a more accurate evaluation method for the CMM emissions, especially for the coal seams with low permeability and strong tectonic deformation in methane outburst coal mines. [ABSTRACT FROM AUTHOR]

The incorporation of spatial and temporal correlations can significantly improve the accuracy of PM 2.5 concentration prediction models. However, the dynamic spatial panel model which explicitly deals with these two correlations remains absent from current approaches to out-of-sample concentration prediction. An issue is that the prediction of daily concentrations for grid points across a vast area may well overwhelm existing algorithms, as it requires an enormous amount of computational resources and an enlarged spatial weight matrix. This paper develops improved algorithms that address this issue. The dynamic spatial panel approaches used in this paper predict daily series PM 2.5 concentrations for grid points covering Mainland China, using daily aerosol, vegetational and meteorological remote sensing data as the explanatory variables. The predicted concentration maps offer more realistic detail in areas where monitoring stations are sparse. Indeed, the error map for the out-of-sample prediction shows that MAPE is less than 30% in most regions, and the average MAPE is 24.28%, which is relatively low compared with similar studies. In contrast to methods which cannot provide coefficients of variables, the developed method offers coefficients to assess the contributions of explanatory variables and temporal-spatial correlation terms, allows quantification of convergence effects, and can distinguish between spillover effects and local effects. A performance comparison of models with various spatial weight matrices shows that model achieves the optimal fitting levels by using the neighbouring unit number threshold of 18 or the distance threshold of 150 km. The case analysis in this paper finds that spillover effects are about three times larger than local effects, and the spatial correlation is greater than the cumulative effects of earlier concentrations. This finding adds further weight to the notion that management of PM 2.5 pollution and associated impacts requires multi-regional and even multi-national coordination and effort. • Increased prediction accuracy by including the temporal-spatial correlation of the pollutant. • Improved dynamic spatial panel approaches that can handle N = 1462 and T = 363 data set. • Out-of-sample prediction with extended spatial weight matrices and optimal thresholds. • Increased frequency and coverage of predicted concentrations using remote sensing data. • Coefficients, convergence effects, local and neighbouring effects for policy analysis. [ABSTRACT FROM AUTHOR]

The recently released Moderate Resolution Imaging Spectrometer (MODIS) Collection 6 introduced a fine scale aerosol optical depth (AOD) distribution, the 3 km product, which is expected to perform well in analyzing aerosols and identifying local air pollution, especially in the severely polluted atmosphere of China. However, few detailed evaluations of regional variations have been conducted. In this paper, we evaluate MODIS 3 km and 10 km AOD products for China against ground-based measurements and compare their performance with respect to spatial and temporal variations. The ground validations indicate that the two products are generally correlated well to ground-based observations. Spatially, the 3 km product slightly outperform the 10 km product in well-developed areas of southern China. Temporally, both products perform worse during spring and summer. Atmospheric clouds and underlying surface are two key factors that influence the accuracy and number of retrievals for both products. The comparison analysis reveals the newly introduced AOD product clearly shows good relationships with the coarse resolution retrievals in spatial and temporal variation but significant differences regarding details. The 3 km AOD product provides better aerosol gradients, more retrievals in bare areas of western China and some spikes of diurnal variation in cloudy days. Seasonal comparisons show the 3 km AOD product is higher than the 10 km product in all seasons, especially during spring and summer. Although the 3 km product for China generally performs slightly worse than the 10 km product, the added information of the MODIS 3 km AOD product shows potential for studying local aerosol characterization, and may facilitate studies of air pollution. [ABSTRACT FROM AUTHOR]

The change of land-use from natural to artificial surface induced by urban expansion can deeply impact the city environment. In this paper, the model WRF/Chem is applied to explore the effect of this change on regional meteorology and air quality over South China, where people have witnessed a rapid rate of urbanization. Two sets of urban maps are adopted to stand for the pre-urbanization and the present urban land-use distributions. Month-long simulations are conducted for January and July, 2014. The results show that urban expansion can obviously change the weather conditions around the big cities of South China. Especially in the Pearl River Delta region (PRD), the urban land-use change can increase the sensible heat flux by 40 W/m 2 in January and 80 W/m 2 in July, while decrease the latent heat flux about −50 W/m 2 in January and −120 W/m 2 in July. In the consequent, 2-m air temperature (T 2 ) increases as much as 1 °C and 2 °C (respective to January and July), planetary boundary layer height (PBLH) rises up by 100–150 m and 300 m, 10-m wind speed (WS 10 ) decreases by −1.2 m/s and −0.3 m/s, and 2-m specific humidity is reduced by −0.8 g/kg and −1.5 g/kg. Also, the precipitation in July can be increased as much as 120 mm, with more heavy rains and rainstorms. These variations of meteorological factors can significantly impact the spatial and vertical distribution of air pollutants as well. In PRD, the enhanced updraft can reduce the surface concentrations of PM 10 by −40 μg/m 3 (30%) in January and −80 μg/m 3 (50%) in July, but produce a correlating increase in the concentrations at higher atmospheric layers. However, according to the increase in T 2 and the decrease in surface NO, the surface concentrations of O 3 in PRD can increase by 2–6 ppb in January and 8–12 ppb in July. Meanwhile, there is a significant increase in the O 3 concentrations at upper layers above PRD, which should be attributed to the increase in air temperature and the enhanced upward transport of O 3 and its precursors. As for some relative small cities, such as Haikou, there is very little variation in surface PM 10 and O 3 in both months, implying less urbanization in these areas. Moreover, the depletion of O 3 by NO may be the main cause of the reduction of O 3 at upper layers in these small cities. [ABSTRACT FROM AUTHOR]

Air pollution has become a serious issue in many developing countries, especially in China, and could generate adverse effects on human beings. Air quality early-warning systems play an increasingly significant role in regulatory plans that reduce and control emissions of air pollutants and inform the public in advance when harmful air pollution is foreseen. However, building a robust early-warning system that will improve the ability of early-warning is not only a challenge but also a critical issue for the entire society. Relevant research is still poor in China and cannot always satisfy the growing requirements of regulatory planning, despite the issue's significance. Therefore, in this paper, a hybrid air quality early-warning system was successfully developed, composed of forecasting and evaluation. First, a hybrid forecasting model was proposed as an important part of this system based on the theory of “decomposition and ensemble” and combined with the advanced data processing technique, support vector machine, the latest bio-inspired optimization algorithm and the leave-one-out strategy for deciding weights. Afterwards, to intensify the research, fuzzy evaluation was performed, which also plays an indispensable role in the early-warning system. The forecasting model and fuzzy evaluation approaches are complementary. Case studies using daily air pollution concentrations of six air pollutants from three cities in China ( i.e. , Taiyuan, Harbin and Chongqing) are used as examples to evaluate the efficiency and effectiveness of the developed air quality early-warning system. Experimental results demonstrate that both the accuracy and the effectiveness of the developed system are greatly superior for air quality early warning. Furthermore, the application of forecasting and evaluation enables the informative and effective quantification of future air quality, offering a significant advantage, and can be employed to develop rapid air quality early-warning systems. [ABSTRACT FROM AUTHOR]

Vehicle emissions have become a major source of air pollution in Shandong province, which has experienced a sharp growth of vehicle numbers in recent years and now has the largest vehicle population in China. This paper combines the COPERT IV model with the vehicle age distribution to estimate the temporal trends and map the spatial distributions of vehicle emissions in Shandong province during the period ranging from 2000 to 2014. Both conventional air pollutants and greenhouse gases are included. In addition, a high-resolution vehicle emission inventory at the prefecture level is developed and mapped on a 0.05° × 0.05° grid based on road information. Our results show that the emissions of all of the conventional air pollutants have decreased to various extents over the recent past, but greenhouse gas emissions have continued to increase due to the lack of effective control strategies. The total emissions of CO, NMVOC, NO X , PM 10 , CO 2 , CH 4 and N 2 O from the Shandong vehicle fleet changed from 1734.5 Gg, 277.9 Gg, 177.0 Gg, 12.4 Gg, 19239.7 Gg, 11.3 Gg and 0.6 Gg, respectively, in 2000 to 1723.3 Gg, 234.2 Gg, 513.8 Gg, 29.5 Gg, 138,419.5 Gg, 15.3 Gg and 3.9 Gg, respectively, in 2014. Vehicle emissions were mainly concentrated in cities and became more dispersed in Shandong province between 2000 and 2014. [ABSTRACT FROM AUTHOR]

Although visibility is a widely-used indicator to quantify the aerosol loadings, only a few studies have been analyzed the representativeness of visibility in deriving Aerosol Optical Thickness (AOT). In this paper, ground-based visibility, MODerate-resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging SpectroRadiometer (MISR) monthly AOT products between July 2002 and December 2014 were analyzed in order to extract the dominant modes of variability using the Singular Value Decomposition (SVD) method. The method has significant merit to reduce data dimension and examine both spatial and temporal variability simultaneously. Results indicated that the satellite retrieved AOTs agreed well with ground-based visibility in terms of inter-annual variability. The correlation coefficients in the first deseasonalized mode are greater than 0.65 between visibility and satellite AOT products. However, large differences were observed in the seasonal variability between ground-based visibility and AOT. In addition, Aerosol vertical distribution from LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies (LIVAS) and cloud data from ground-based meteorological station were used to investigate the seasonal variability disagreement. The AOT values derived from LIVAS extinction coefficients between 0 and 500 m above surface have a stronger relationship with visibility, than total column AOT with visibility. It also indicates that seasonal variation of aerosol vertical distribution is the main cause of the disagreement between two parameters, and the uncertainties of satellite products also contribute to the disagreement. Results in this study highlighted that the visibility observation could only be used to depict the inter-annual AOT and more ancillary information could be used for studying seasonal AOT variation. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO 2) is an important target for monitoring atmospheric quality. Deriving ground-level NO 2 concentrations with much finer resolution, it requires high-resolution satellite tropospheric NO 2 column as input and a reliable estimation algorithm. This paper aims to estimate the daily ground-level NO 2 concentrations over China based on machine learning models and the TROPOMI NO 2 data with high spatial resolution. In this study, four tree-based algorithm machine learning models, decision trees (DT), gradient boost decision tree (GBDT), random forest (RF) and extra-trees (ET), were used to estimate ground-level NO 2 concentrations. In addition to considering many influencing factors of the ground-level NO 2 concentrations, we especially introduced simplified temporal and spatial information into the estimation models. The results show that the extra-trees with spatial and temporal information (ST-ET) model has great performance in estimating ground-level NO 2 concentrations with a cross-validation R2 of 0.81 and RMSE of 3.45 μg/m3 in test datasets. The estimated results for 2019 based on the ST-ET model achieves a satisfactory accuracy with a cross-validation R2 of 0.86 compared with the other models. Through time-space analysis and comparison, it was found that the estimated high-resolution results were consistent with the ground observed NO 2 concentrations. Using data from January 2020 to test the prediction power of the models, the results indicate that the ST-ET model has a good performance in predicting ground-level NO 2 concentrations. Taking four ground-level NO 2 concentrations hotspots as examples, the estimated ground-level NO 2 concentrations and ground-based observation data during the coronavirus disease (COVID-19) pandemic were lower compared with the same period in 2019. The findings offer a solid solution for accurately and efficiently estimating ground-level NO 2 concentrations by using satellite observations, and provide useful information for improving our understanding of the regional atmospheric environment. • The spatiotemporal information can enhance the estimated ability of the models. • ST-ET model has high accuracy in estimating ground NO 2 concentration with high R2 and low RMSE. • The estimated results can capture the spatial and temporal pattern especially during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Particulate air pollution has had a significant impact on human health in China and it is associated with cardiovascular and respiratory diseases and high mortality and morbidity. These health impacts could be translated to reduced labor availability and time. This paper utilized a supply-driven input-output (I-O) model to estimate the monetary value of total output losses resulting from reduced working time caused by diseases related to air pollution across 30 Chinese provinces in 2007. Fine particulate matter (PM 2.5 ) pollution was used as an indicator to assess impacts to health caused by air pollution. The developed I-O model is able to capture both direct economic costs and indirect cascading effects throughout inter-regional production supply chains and the indirect effects greatly outnumber the direct effects in most Chinese provinces. Our results show the total economic losses of 346.26 billion Yuan (approximately 1.1% of the national GDP) based on the number of affected Chinese employees (72 million out of a total labor population of 712 million) whose work time in years was reduced because of mortality, hospital admissions and outpatient visits due to diseases resulting from PM 2.5 air pollution in 2007. The loss is almost the annual GDP of Vietnam in 2010. The proposed modelling approach provides an alternative method for health-cost measurement with additional insights on inter-industrial and inter-regional linkages along production supply chains. [ABSTRACT FROM AUTHOR]

In this paper, the spatial dynamics of the atmospheric particulate matters (resp. PM 10 and PM 2.5 ) are studied using turbulence methodologies. It is found experimentally that the spatial correlation function ρ ( r ) shows a log-law on the mesoscale range, i.e., 50 ≤ r ≤ 500 km, with an experimental scaling exponent β = 0.45. The spatial structure function shows a power-law behavior on the mesoscale range 90 ≤ r ≤ 500 km. The experimental scaling exponent ζ ( q ) is convex, showing that the intermittent correction is relevant in characterizing the spatial dynamic of particulate matter. The measured singularity spectrum f ( α ) also shows its multifractal nature. Experimentally, the particulate matter is more intermittent than the passive scalar, which could be partially due to the mesoscale movements of the atmosphere, and also due to local sources, such as local industry activities. [ABSTRACT FROM AUTHOR]

Recently, the PM 2.5 pollution in China has occurred frequently and caused widely concern. In order to identify the key factors for PM 2.5 generation, the formation characteristics of PM 2.5 would be revealed. A property of electric neutrality of PM 2.5 was proposed under the least-energy principle and verified through electricity-charge calculation in this paper. It indicated that PM 2.5 is formed by the effect of electromagnetic force, including the effect of ionic bond, hydrogen bond and polarization. According to the analysis of interactive forces among different chemical components, a simulation model is developed for describing the random process of PM 2.5 generation. In addition, an orthogonal test with two levels and four factors has been designed and carried out through the proposed model. From the text analysis, PM 2.5 would be looser and suspend longer in atmosphere due to Organic Compound ( OC ) existing ( OC can reduce about 67% of PM 2.5 density). Considering that N H 4 + is the only cation in the main chemical components of PM 2.5 , it would be vital for anions (such as S O 4 2 − and N O 3 − ) to aggregate together for facilitating PM 2.5 growing. Therefore, in order to relieve PM 2.5 pollution, control strategies for OC and N H 4 + would be enhanced by government through improving the quality of oils and solvent products, decreasing the amount of nitrogenous fertilizer utilization, or changing the fertilizing environment from dry condition to wet condition. [ABSTRACT FROM AUTHOR]

The aim of this paper is to evaluate the diurnal variation in soil respiration under different land use types on Taihang Mountain, North China, and to understand its response to environmental factors (e.g., soil temperature and moisture) and forest management. Diurnal variations in soil respiration from plantations ( Robinia pseudoacacia, Punica granatum , and Ziziphus jujuba ), naturally regenerated forests ( Vitex negundo var. heterophylla ), grasslands ( Bothriochloa ischaemum ), and farmlands (winter wheat/summer maize) were measured using an LI-8100 automated soil CO 2 flux system from May 2012 to April 2013. The results indicated that land use type had a significant effect on the diurnal variation of soil respiration. The diurnal soil respiration from farmlands was highest, followed by Ziziphus jujube, R. pseudoacacia, P. granatum , the lower soil CO 2 efflux was found from B. ischaemum and V. negundo var. heterophylla . The diurnal soil respiration across different land use types was significantly affected by soil temperature and moisture, and their interaction. Precipitation-stimulated soil respiration increased more in soil with low water content and less in soil with high water content. The lower diurnal soil respiration from naturally regenerated forests suggests that naturally regenerated vegetation is the optimal vegetation type for reducing global warming. [ABSTRACT FROM AUTHOR]

The analysis and forecasting of PM concentrations play a significant role in regulatory planning on the reduction and control of PM emission and precautionary strategies. However, accurate PM forecasting, which is needed to establish an early warning system, is still a huge challenge and a critical issue. Determining how to address the accurate forecasting problem becomes an even more significant and urgent task. Based on gray correlation analysis (GCA), Ensemble Empirical Mode Decomposition (EEMD), Cuckoo search (CS) and Back-propagation artificial neutral networks (BPANN), this paper proposes the CS-EEMD-BPANN model for forecasting PM concentrations. Prior to establishing this model, gray correlation has been uniquely used to search for possible predictors of PM among other air pollutants (CO, NO 2 , O 3 and SO 2 ) and meteorological environments (wind speed, wind direction, temperature, humidity and pressure). The proposed method was investigated in four major cities of China (Beijing, Shanghai, Guangzhou and Lanzhou) with different characteristics of climatic, terrain and emission sources. The results of the gray correlation analysis indicate that CO, NO 2 and SO 2 are more related to PM and that the incorporation of these predictors can significantly improve the model performance predictability, suggesting the effectiveness of our developed method. [ABSTRACT FROM AUTHOR]

A thermal internal boundary layer (TIBL) can cause the fumigation effect and reduce the dispersion capacity of air pollutants. The paper's goal is to quantify the interactions of pollutants between cities subjected to enhanced fumigation effects. To that end, an observational campaign was launched at the Shandong Peninsula of China in the winter of 2020. The campaign focused on monitoring vertical profiles of aerosols, wind field, temperature, and humidity for the boundary-layer dynamic-thermodynamic structure. The Nested Air Quality Prediction Modeling System (NAQPMS) coupled with online source-tagging model was also employed to delve into the source-receptor relationships of the cities involved in the fumigation effect. The results indicated that the presence of a TIBL structure triggered the prolonged and widespread pollution episodes. This resulted in a temperature inversion (250–1000 m in altitude) near the convergence line on surface and rendered the atmospheric boundary layer more stably stratified, i.e., a situation that was unfavorable for the diffusion of pollutants against a persistently sinking flow (with a mean velocity of roughly 0.2 m s−1) for a prolonged time. Meanwhile, TIBLs prevented the fumigation areas from interacting with adjacent inland areas (Shandong Peninsula's local contribution accounting for >90% of the PM 2.5) and facilitated inter-city transport of pollutants inside this peninsula (the transport contribution between cities reached up to 55–75%). Such distinct behaviors as observed over this coastal area render air pollution control more of an elusive issue, as is referenced to several inland cities. [Display omitted] ● Quantifying the interactions of pollutants between cities subjected to enhanced fumigation effects ● The presence of a TIBL structure triggered the prolonged and widespread pollution episodes ● TIBLs made the atmospheric boundary layer more stably stratified and unfavorable diffusion of pollutants with strong sinking flowing ● TIBLs prevented fumigation areas from interacting with adjacent inland areas and facilitated inter-city transport of pollutants inside it [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new classification of industrial sector on basis of a source-tracing method for the historical industrial emissions of non-methane volatile organic compounds (NMVOCs). The industrial sources were divided into four major source categories: production of NMVOCs, storage and transport, industrial processes using NMVOCs as raw materials, and processes using NMVOCs-containing products. Multiyear emission inventories of NMVOCs from industrial sources were established for the period of 1980–2010 in China, covering 98 contributing industrial sources. The inventories were developed by the emission factor method, and further gridded at a spatial resolution of 50 km × 50 km based on GIS methodology, using population data as spatial surrogate. The result showed that China's industrial NMVOCs emissions had increased by 11.6 times at an average annual rate of 8.5% from 1.15 Tg in 1980 to 13.35 Tg in 2010. The four major source categories generated 19.6% (2.63 Tg, production of NMVOCs), 9.6% (1.295 Tg, storage and transport), 13.2% (1.769 Tg, industrial processes using NMVOCs as raw materials), and 57.4% (7.662 Tg, processes using NMVOCs-containing products) of the total emissions from China in 2010. Moreover, the top four industrial emissions sources were oil refinery, architectural decoration, machinery equipment and printing, accounted for 48.3% all together. With respect to the spatial distribution, the emissions mostly occurred in the eastern, northern and southern parts of China, all being much higher than that in the western part. The top four emission provinces were Shandong, Jiangsu, Guangdong, and Zhejiang, accounting for 38.3% of the national emissions. As for the past 30 years, the national industrial NMVOCs emissions increased year-by-year, being in pace with the accelerated industrialization process and the sharp economic growth, especially the rapid expansion of the high-emission industries. Particularly, the oil refinery, product oil and solvent distribution, architectural decoration, and mechanical equipment manufacture became the four largest contributors, accounting for 15.9–34.3%, 6.5–10.2%, 7–8%, and 6–7% of national industrial NMVOCs. [Copyright &y& Elsevier]

Abstract: This paper presents a multi-scale study on formation process of urban pollution and haze clouds as well as their interactions over northern China in spring using integrated satellite and surface observations. Several extreme haze events occurred in Beijing area in March 2013, but primary atmospheric pollutants in the urban pollution exhibited inconsistent variations with the widespread haze clouds observed by satellites. Two typical types of haze event were found in Beijing area. Type-1 haze pollution appeared in stagnant weather conditions, during which PM2.5 was <200 μg m−3 with a short duration within 1–2 days. By contrast, strong northwestern winds prevailed in type-2 haze events with durative and intense temperature inversion near surface. Meanwhile, PM2.5 concentration exceeded ∼400 μg m−3 in type-2 pollution, and the heavy pollution can last 3–5 days. Different from urban pollution, our results show that the thick haze clouds were dominated by mixing of floating dust and anthropogenic pollutants in the middle and upper part, accompanied by hygroscopic growth of fine particles. Elevated coarse dust particles were prevalent over northern China, which accounted for a predominant fraction in the columnar optical volume during all the haze events. Furthermore, comparison between satellite and surface observations indicates that haze clouds above surface had no significant direct contribution to the serious urban pollution. In addition, mixing of dust and anthropogenic pollutants at high altitudes regulates regional aerosol optical properties throughout the whole March. [Copyright &y& Elsevier]

Abstract: The lung cancer mortality rate in the rural area of the Xuan Wei, Yunnan, is among the highest in China, especially in women. In this paper, the coal-burning indoor and corresponding outdoor PM10 samples were collected at the Hutou village, representing the case of high lung cancer rate, and the Xize village, representing the case of low lung cancer rate. Plasmid scission assay was used to investigate the bioreactivity of the PM10. The inductively coupled plasma-mass spectrometry (ICP-MS) was employed to investigate the trace element compositions of the PM10. The results showed that the oxidative damage caused by both indoor and outdoor PM10 at the Hutou village was obviously higher than that at the Xize village, with the indoor PM10 having higher oxidative damage than corresponding outdoors. Among all analyzed samples, the indoor night PM10 samples from the Hutou village have the highest oxidative capacity. The levels of total water-soluble elements had a higher level in the PM10 of the Hutou village than that of the Xize village. It is interesting that the levels of water-soluble As, Cd, Cs, Pb, Sb, Tl and Zn in PM10 had better positive correlation with DNA damage rates, implying that these elements in their water-soluble state should be one of the main factors responsible for the high oxidative capacity of PM10, thus possibly the higher lung cancer rates, at the Hutou village. [Copyright &y& Elsevier]

Beryllium-7 is a potentially powerful tracer of soil erosion, but information on 7Be atmospheric deposition and associated soil inventories on the Loess Plateau of China is not readily available. In the study reported in this paper, we measured the 7Be inventories in undisturbed soil at different sampling times on the northern Loess Plateau of China for three years, between 2010 and 2012, and estimated the 7Be deposition fluxes and the daily 7Be inventories in undisturbed soil. The annual 7Be deposition fluxes during this period varied between 1303 ± 119 and 2222 ± 147 Bq m−2, with a mean of 1759 ± 416 Bq m−2. There is a marked seasonality for the 7Be deposition fluxes with the maximum in summer, approximately 50% to the annual deposition flux, and the minimum in winter, approximately 5% to the annual deposition flux. Precipitation amounts can explain more than 70% of the variation in 7Be deposition flux. 7Be deposition in the form of dustfall, dew and frost make a significant contribution to the 7Be deposition flux in the study region. The daily 7Be inventories in undisturbed soil varied markedly through time and ranged between 89.2 and 941.8 Bq m−2 with a mean of 392 ± 210 Bq m−2. They demonstrated a unimodal distribution over the year, with the highest values in August or September and the lowest in late winter or early spring. [ABSTRACT FROM AUTHOR]

To improve PM 2.5 predictions in Northeast Asia, we estimated a new background error covariance matrix (BEC) for aerosol data assimilation using surface PM 2.5 observations. In contrast to the conventional method of BEC estimation that uses perturbations in meteorological data, this method additionally considered the perturbations using two different emission inventories. By taking the emission uncertainty into account, we found that the standard deviations in the BEC were significantly increased. The standard deviations became around three times larger than those in the conventional method at the surface. The impacts of the new BEC were then tested for the prediction of surface PM 2.5 over Northeast Asia using the Community Multiscale Air Quality (CMAQ) model initialized by three-dimensional variational method (3D-VAR). The surface PM 2.5 data measured at 154 sites in South Korea and 1535 sites in China were assimilated every 6 h during the campaign period of the Korea-United States Air Quality Study (KORUS-AQ) (1 May–14 June 2016). The data assimilation with the new BEC showed better agreement with the surface PM 2.5 observations than with the BEC from the conventional method. Our method was also more consistent with the observations in 24-h PM 2.5 predictions than the conventional method (specifically, with a ∼44% reduction of negative biases). We concluded that increased standard deviations, together with updated horizontal and vertical length scales in the new BEC, improved the data assimilation and short-term predictions of the surface PM 2.5. This paper also suggests several research efforts to further improve the BEC for better short-term PM 2.5 predictions in Northeast Asia. [Display omitted] • A new background error covariance (BEC) for PM 2.5 predictions was developed. • The new BEC considers the uncertainties in anthropogenic emissions. • Two different emission inventories were used to account for the uncertainty. • 24-h PM 2.5 predictions were improved with ∼44% fewer negative biases. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, heavy metal pollution accidents were reported frequently in China. The atmospheric heavy metal pollution is drawing all aspects of attention. This paper summarizes the recent research results from our studies and previous studies in recent years in China. The level, temporal variation, seasonal variation and size distribution of the heavy metals of atmospheric Lead(Pb), Vanadium(V), Manganese(Mn), Nickel(Ni), Chromium(Cr), Cadmium(Cd), Copper(Cu), Zinc(Zn) and Arsenic(As) were characterized in China. The emission characteristics and sources of atmospheric heavy metals and As in China were reviewed. Coal burning, iron and steel industry and vehicle emission are important sources in China. Control policies and effects in China were reviewed including emission standards, ambient air quality standards, phase out of leaded gasoline and so on, and further works for atmospheric heavy metals control were suggested. The comprehensive heavy metals pollution control measures and suggestions were put forward based on the summarization of the development and experience of the atmospheric heavy metal pollution control abroad. [Copyright &y& Elsevier]

Abstract: In this paper, we introduce a new method to estimate the change in mean mixing ratio of a target species at a receptor site due to the contribution of the long-range transport (CLRT). We applied our method to determine inter-annual and inter-seasonal variations in the CLRT of SO2 in Seoul, a major megacity in northeast Asia, during the period from 2001 to 2010. The major potential source areas of SO2 for the 2001–2010 period were located in East China according to the potential source contribution function (PSCF) maps. The CLRT of SO2 in Seoul was estimated to range from 0.40 to 1.03 ppb, which accounted for 8–21% of the ambient mean SO2 mixing ratio in Seoul. The inter-annual variations of estimated CLRT of SO2 was well correlated with those of the total emissions in China during the period of 2001–2008 (R = 0.85). We found that both local emissions from around Seoul and long-range transport from East China, especially the Shandong peninsula, affected the SO2 mixing ratio in Seoul throughout the decade of study. The CLRT of SO2 in Seoul increased after 2007 even though the total emissions of SO2 by China have been decreasing since 2006. The CLRT of SO2 in Seoul was high in spring and winter, which can be attributed to enhanced SO2 emissions in East China during these seasons and a dominant westerly wind. The CLRTs of SO2 accounted for 15, 11, 4, and 12% of the seasonal mean SO2 mixing ratio in spring, summer, fall, and winter, respectively. The uncertainty ranged from 24 to 62% of the estimated CLRT values. [Copyright &y& Elsevier]

Abstract: The effects of black carbon (BC) aerosol on climate warming have been the study focus in the recent decade, and the reduction of BC is now expected to have significant near-term climate change mitigation. Large uncertainties of BC optical properties, however, still exist and seriously restrict the ability to quantify BC''s climate effects. In this study, advanced instrumentation (a three-wavelength photoacoustic soot spectrometer (PASS-3) and a single particle soot photometer (SP2)) were used to measure black carbon aerosol and analyze its optical properties in a mega-city in South China, Shenzhen, during the summer of 2011. The results indicated that the average BC mass concentration was 4.0 ± 3.1 μg m−3 during the campaign, accounting for ∼11% of the total PM2.5 mass concentration. The PM2.5 light absorption at 405, 532 and 781 nm was 37.1 ± 28.1, 25.4 ± 19.0 and 17.6 ± 12.9 Mm−1, respectively. The average absorption Angstrom exponent of PM2.5 in visual spectrum (AAE405–781 nm) was 1.1 ± 0.1 during the campaign, indicating that the light absorbing carbon mainly came from vehicular emissions, with little contributions from biomass burning emissions. The mass absorption efficiency (MAE) of BC at 532 nm ranged from 5.0 to 8.5 m2 g−1 during the campaign, with an average of 6.5 ± 0.5 m2 g−1, and showed an obvious diurnal pattern with high values in the daytime. The average percentage of internally mixed BC was 24.3 ± 7.9% during the campaign, showing significant positive correlation relationship with the MAE of BC. More quantitative data analysis indicated that the internally mixed BC would amplify MAE by about 7% during the campaign, which stands in accordance with the new finding of a very recent Science magazine paper (Cappa et al., 2012) that the BC absorption enhancement due to internal mixing in the real atmosphere is relatively low, in apparent contrast to theoretical model predictions. [Copyright &y& Elsevier]

Abstract: There are two kinds of primary emissions of PCBs to the atmosphere; one is primary emissions due to intentionally produced PCBs (IP-PCBs), and the other is due to unintentionally produced PCBs (UP-PCBs). This paper presents UP-PCB emissions in China from 1950 to 2010. The total UP-PCB emissions in the 61 years were estimated to be 146.1 t, approximately 98.1% of which were from cement (132.5 t) and steel industries (6.3 t). The emissions from Chinese coking industry were 115 kg from 1950 to 2010, less than 0.1% of the total UP-PCB emissions in China. Both the temporal and spatial trends of these UP-PCB emissions were investigated. The UP-PCB emissions started to increase quickly at the middle of 1980s due to the fast and continuously developing cement and steel industries in China, and exceeded IP-PCB emissions possibly during the beginning of 2000s. The spatial pattern of UP-PCB emissions was in general consistent with the population distribution pattern, indicating the anthropogenic nature of the UP-PCB emissions. The gridded Chinese emission inventories for UP-PCBs from cement and steel industries on a 1/6° latitude and 1/4° longitude resolution were also compiled. The gridded UP-PCB emissions in 2005 were compared with PCB air concentrations measured by our group (IJRC-PTS) in 2005, and a significant correlation between these 2 datasets was found for the rural sites, but not for the urban sites. It is most likely that these UP-PCB emissions were a major source for PCB in Chinese rural air, and possibly responsible for the increase of PCBs in Chinese air as a whole from 2004 to 2008, reported in recent publications. To our knowledge, this is the first dataset of the Chinese UP-PCB emissions spanning 61 years from 1950 to 2010, and also the first gridded emission inventories for UP-PCBs on a national scale. [Copyright &y& Elsevier]

Abstract: This paper aims to study the vehicular emissions trends in the Beijing–Tianjin–Hebei (BTH) region, located in northern China. The multiyear emission inventories of NO X , CO, VOC and PM10 from road vehicles in the period 1999–2010 were developed by the COPERT IV model. Results show that vehicular emissions of CO and VOC have decreased by annual average change rates (AACR) of −3.1% to −5.2% and −4.4% to −6.9% in the study area, respectively. However, due to the rapid development of freight traffic, emissions of NO X and PM10 have kept increasing in Tianjin and Hebei. Based on the vehicular emission inventories, trends of emission levels for vehicles with different standards, as well as the overall effects of implementing vehicular emission mitigation strategies were assessed. It is suggested that passenger cars (PC) with Euro 0 and Euro I standards, which were at higher emission level in the PC fleet, should be gradually eliminated. Although the increasing rates (IR) of emissions from PC were lower than those of the PC population, the sharp growth of PC population in recent years contributed to a remarkable increase of emissions, weakening the overall mitigation effect. Total vehicle population capacity and other mitigation measures should be studied in China in order to develop new and more effective vehicular emission control strategies. [Copyright &y& Elsevier]

Abstract: This paper presents results of measurements of tropospheric sulphur dioxide (SO2) from satellite over China during 2004–2009. SCIAMACHY/ENVISAT SO2 data products have been validated by ground based remote sensing instrument MAXDOAS in China, and with predictions of the atmospheric model GEOS–Chem. The spatial and temporal distribution of tropospheric SO2 over China is discussed in this study. The result shows that the SO2 load over East China is decreasing since strong control for pollution emission in 2007 for preparation of 2008 Olympic Games in China, while the SO2 load in West China is increasing all the way during 2004–2009, which might reflect that the anthropogenic activity was added to promote the economy development in west of China. Typical seasonal variation with high pollution levels in winter and low in summer is found in the northwest of China, while the inverse seasonal variation is found for the south of China. The characteristics of tropospheric SO2 over the major cities in China were explored and found that tropospheric SO2 was partly under control from 2007 because of the policy from China government for reduction in SO2 emissions in 2006. And the SO2 value shows remarkably decrease in most of the major cities after 2007 because strong control for the pollution emission for 2008 Olympic games. Guangzhou city shows high SO2 pollution levels in summer time, since most of the coal power plants and thermal power industry are located to the south of Guangzhou city and southerly winds dominate during summer time. [Copyright &y& Elsevier]

Abstract: Civil aviation in China has developed rapidly in recent years, and the effects of civil aviation emissions on the atmospheric environment should not be neglected. The establishment of emission inventories of atmospheric pollutants from civil aviation contributes to related policy formation and pollution control. According to the 2010’s China flight schedules, aircraft/engine combination information and revised emission indices from the International Civil Aviation Organization emission data bank based on meteorological data, the fuel consumption and HC, CO, NO x , CO2, SO2 emissions from domestic flights of civil aviation in China (excluding Taiwan Province) in 2010 are estimated in this paper. The results show that fuel consumption in 2010 on domestic flights in China is 12.12 million tons (metric tons), HC, CO, NO x , CO2 and SO2 emissions are 4600 tons, 39,700 tons, 154,100 tons, 38.21 million tons and 9700 tons, respectively. The fuel consumption and pollutant emissions of China Southern Airline are responsible for the largest national proportion of each, accounting for 27% and 25–28%, respectively. [Copyright &y& Elsevier]

Abstract: Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and almucantar retrievals (single scattering albedo (SSA) and aerosol size distribution) from 2005–2009 in Beijing and Kanpur are used to analyze differences and similarities in aerosol optical properties over these two regions. The examination of monthly mean AOD (440 nm) shows that maximum and minimum values occurred in summer and winter, respectively, for Beijing, while the range in AOD in Kanpur was lower. Precipitation in both Beijing and Kanpur peaked in summer; however, the columnar water vapor (CWV) exhibited a high correlation with AOD in Beijing (R 2 = 0.79) but had a weak relationship with AOD in Kanpur (R 2 = 0.13). The Angstrom exponent (α, 440–870 nm) generally increased linearly as the fine mode fraction (FMF) of AOD (500 nm) increased for FMF < 90% in both regions, with a high correlation of R 2 > 0.96. However a clear decrease in α for FMF > 90% found in Beijing is not shown distinctly in Kanpur, and is mainly due to the higher aerosol loading in this FMF bin at Beijing (AOD at 440 nm > 2.2) which results in a stronger coagulation of fine mode particles. Bimodal seasonally-averaged size distributions reveals similar aerosol mixtures composed of fine pollution particles and coarse dust particles in both regions. The analysis of spectral SSA as a function of α is emphasized in this paper. The average SSA at 440 nm in both regions shows a similar low dynamic range of ∼0.03 for α < 1.4. The obvious increase in SSA at 440 nm for α > 1.4 in both regions can be attributed to a higher FMF leading to fine mode coagulation. However, the distinctly smaller increase in Kanpur suggests that fine mode aerosols at Beijing are less absorbing than those at Kanpur. The visibly lower SSA at 675 nm at Kanpur compared to that at Beijing for α > 0.4 is due to a larger find-coarse mode separation radius of ∼0.76 μm in Beijing versus a value lower than ∼0.58 μm in Kanpur. Another reason lies in the weaker absorption by fine mode aerosols in Beijing. The distinctly lower near-infrared SSA in Kanpur when α < 1.4, compared to Beijing, may be attributed to lower aerosol concentrations in all α bins, resulting in less aggregation of absorbing black carbon particles on coarse particles in Kanpur. The classification of aerosol properties shows that the AOD accumulation mode in all seasons, even including spring, in Beijing and in post-monsoon and winter seasons in Kanpur can be attributed to fine particle coagulation or hygroscopic growth; during pre-monsoon and monsoon seasons in Kanpur, it is due to coarse mode particle accumulation or cloud contamination. [Copyright &y& Elsevier]

Abstract: This paper analyzes TOMS AOD at 500 nm (1980–2001), along with MODIS data (2000–2008) at 550 nm to investigate variations at one-degree grid over eight typical regions in China and the trends in AODs, temporally and spatially. In contrast to recently reported global decrease in AOD over global ocean beginning around 1990, we find there virtually exists no apparent AOD transition in China for that: firstly no notable upward tendencies in AOD during 1980–1992 for the relative low value (+0.001/decade), then during 1996–2001 a discernible ascending tendency with larger magnitude at 0.01/decade, and finally, since 2000, a weak upward trend with +0.004/decade. The large increases during 1996–2001 are presumably consequences of large increases in industrial activities and bear a strong resemblance to the long-term decreasing observations of incident solar radiation and cloud cover in China. Specifically, in late 1990’s, only in Taklimakan Desert a negative trend with a maximum magnitude of −0.04/decade is detected. However, over regions such as Jingjinji and Pearl River Delta influenced by industrial activities, positive tendencies at +0.01/decade are observed. Seasonal patterns in the AOD regional long-term trend are evident. AODs exhibit generally similar seasonality and the summer dominates higher AOD value than the autumn. In particular, during the period 1980–2001, all the eight regions except Taklimakan Desert witness the maximum aerosols in winter while there is not such seasonality during the period 2000–2008. Geographically, we also document spatial patterns of AOD variations over China. Results reveal that no apparent upward trends in AOD (about 15% per decade) are observed in 1980’s, while beginning 1990 till 2008, both data (TOMS and MODIS) are indicative of a significant AOD increase across China, especially in 1990’s it is indeed the case, roughly in accordance with the overall trends at regional scale. [Copyright &y& Elsevier]

Abstract: Vertical distributions of air pollutants are crucial for understanding the key processes of atmospheric transport and for evaluating chemical transport models. In this paper, we present measurements of non-methane hydrocarbons (NMHCs) and halocarbons obtained from an intensive aircraft study over northeast (NE) China in summer 2007. Most compounds exhibited a typical negative profile of decreasing mixing ratios with increasing altitude, although the gradients differed with different species. Three regional plumes with enhanced VOC mixing ratios were discerned and characterized. An aged plume transported from the northern part of the densely populated North China Plain (NCP; i.e. Beijing–Tianjin area) showed relatively higher levels of HCFC-22, 1,2-dichloroethane (1,2-DCE) and toluene. In comparison, the plume originating from Korea had higher abundances of CFC-12, tetrachloroethene (C2Cl4) and methyl chloride (CH3Cl), while regional air masses from NE China contained more abundant light alkanes. By comparing these results with the earlier PEM-West B (1994) and TRACE-P (2001) aircraft measurements, continuing declining trends were derived for methyl chloroform (CH3CCl3), tetrachloromethane (CCl4) and C2Cl4 over the greater China–northwestern Pacific region, indicating the accomplishment of China in reducing these compounds under the Montreal protocol. However, the study also provided evidence for the continuing emissions of several halocarbons in China in 2007, such as CFCs (mainly from materials in stock) and HCFCs. [Copyright &y& Elsevier]

Abstract: This paper presents the first detailed investigation on airborne trace metals and their potential major sources at Lhasa, the largest city in Tibetan Plateau (TP). The whole year PM10 samples were collected during September 2007 and August 2008. The annual average concentration of PM10 in Lhasa was 51.8±42.5μgm−3, lower than those of major Asian cities. Distinct seasonal patterns were observed in PM10 concentration, with higher concentrations in winter, and lower in summer. The mean elemental concentrations were generally comparable with other urban areas, but significantly higher than those from a remote site in TP (i.e., Nam Co). Crustal elements, including Na, Mg, Al, K, Ca, Sc, Ti, V, Mn, Fe, As and Ba, had similar seasonal patterns in PM10, while other elements, such as Cr, Co, Ni, Cu, Zn, and Cd, had less distinct seasonal variations, suggesting more anthropogenic inputs of the latter group. The result of principle component analysis (PCA) on trace elements demonstrated that fugitive dusts, traffic emissions and waste incineration activities were probably the major sources of anthropogenic metals in the atmosphere at Lhasa. The Pb isotopic compositions revealed that the metal was mainly originated from nature background with a minor contribution from the cement factory. The data obtained in this study can be useful for making pollution control strategies in the city, and also valuable for trace element studies in other environmental medium, such as snow, ice core, and lake sediments in the TP region. [Copyright &y& Elsevier]

Abstract: VOC pollution in indoor air has become a serious problem in urban areas of China. Building materials and furniture used indoors are the main sources of such pollution. How to determine the VOC emission characteristics of furniture rapidly and conveniently is an important but difficult problem. In this paper, we extend the C-history method for determining the VOC emission characteristic parameters (i.e., the initial emittable VOC concentration, C 0, the partition coefficient, K, and the diffusion coefficient, D) of homogeneous building materials developed by to measure the parameters of VOC emissions from wood-based furniture in a 30m3 full-scale chamber. The conditions under which there are three equivalent parameters, C 0,equ, K equ and D equ for multi-layer board and wood-based furniture is analyzed and presented. A series of tests were performed to determine the three parameters of some commonly used wood-based furniture in China. A run of independent ventilated chamber experiments were carried out to validate the method. The results show that: (1) The agreement of the formaldehyde and six VOCs concentrations between the ventilated experiment and the simulated results based on the determined parameters is very good; (2) The method could be used in testing multi-layer board and wood-based furniture. The testing time is usually less than 3 days as compared with the furniture testing method typically performed under ventilated chamber conditions and take 5,7,14 or 28 days. The possibilities and limitations of applying the method to the existing furniture labeling schemes are discussed. [Copyright &y& Elsevier]

Abstract: The chemical compositions of organic macromolecules in dust are very complex and have not yet been investigated in detail. In this paper, we study the organic macromolecules in 12 dust samples collected from an urban and suburban area of Guangzhou, China. Organic macromolecules in the dust were firstly separated into humic acids (HA), kerogen and black carbon (KB) and black carbon (BC) fractions by chemical treatment with NaOH, HCl/HF and K2Cr2O7/H2SO4, respectively. The isolated fractions were subsequently characterized using elementary analysis (EA), organic petrographic examination (OPE), scanning electron microscopy (SEM), and solid state 13C cross-polarization and magic angle spinning nuclear magnetic resonance spectroscopy (13C-CP/MAS NMR) etc. The results showed that the major organic macromolecules were KB, which accounted for 64.8%–95.8% of total organic carbon (TOC) in the dusts. Isolated HAs were characterized by high H/C, N/C and O/C atomic ratios, and high carboxyl, methoxyl and aliphatic carbon contents in the overall carbon structure; meanwhile, kerogens (K) showed lower H/C, N/C and O/C atomic ratios than those of HAs and had chemical compositions enriched in hydroxyl carbon; further, the optical features of K were similar to those of “vitrinite”. BC is formed from the combustion of biomass and fossil fuels, so that aromatic and aliphatic carbons are the most important components in its carbon skeleton. Under the microscope, BC appeared to belong to the petrographic groups, such as “semifusinte” and “fusinite”, and showed a distinct texture when observed by SEM. Compared with the HAs and K isolated from soils and sediments, HAs in dust were relatively lower in aromatic carbon and K in dust was always “type III”. These results are strongly consistent with the dust samples originating from the photochemical degradation of volatile organic compounds, in contrast to the geological HAs and K which are sourced from the biodegradation of lignin and algae or from bacterial activities in water and sediment. [Copyright &y& Elsevier]