Emissions In Shanghai Research Articles

Comparative analysis of methods for seasonal particulate organic nitrate estimation in urban areas

Accurately estimating particulate organic nitrate under high NOx and oxidizing conditions is critical. This study compared the NOx+ ratio, unconstrained Positive Matrix Factorization (PMF), and Multilinear Engine-2 (ME2) methods to estimate particulate organic nitrate in Shanghai across different seasons. The factors associated with organic nitrate, as identified through two receptor methods, exhibited consistent daily patterns in spring, summer, and autumn, although source contributions varied. The NOx+ ratio method reported higher organic nitrate levels than the PMF and ME2 methods, likely due to the fixed RON/RAN parameter. Seasonal RON/RAN parameters were optimized based on precursor emissions in Shanghai, achieving values of 3.13 in spring, 2.25 in summer, and 1.88 in autumn. This optimization reduced discrepancies in organic nitrate using the NOx+ ratio to 3.2–7.4%. The optimized parameters in this study support the rapid and accurate estimation of organic nitrate during different seasons in urban areas.

Sources of PM2.5 exposure and health benefits of clean air actions in Shanghai.

Estimating PM2.5 exposure and its health impacts in cities involves large uncertainty due to the limitations of model resolutions. Consequently, attributing the sources of PM2.5-related health impacts at the city level remains challenging. We characterize the health impacts associated with chronic PM2.5 exposure and anthropogenic emissions in Shanghai using a chemical transport model (GEOS-Chem) and its adjoint. By incorporating high-resolution satellited-derived PM2.5 estimates into the calculation, we investigate the response of PM2.5 exposure and its related health impacts in Shanghai to changes in anthropogenic emissions from each individual region, species, sector, and month. We estimate that a 10% decrease in anthropogenic emissions throughout China avoids over 752 (506-1,044) PM2.5-related premature deaths in Shanghai, with changes in local emissions potentially saving 241 (161-334) lives. Ammonia (NH3) emissions are identified as the marginal dominant contributor to the health impacts due to the NH3-limited PM2.5 formation within the city, thus controlling NH3 emissions at both the local and regional scales are effective at reducing the population's exposure to PM2.5. A negative response of the PM2.5 exposure to local nitrogen oxides (NOx) emission changes is detected in winter. Even so, controlling NOx emissions is still justified since the negative impacts decrease as anthropogenic emissions decline and NOx emission reductions benefit the public health on average. The anthropogenic emission changes due to Clean Air Actions helped avoid 3,132 (2,108-4,346) PM2.5-related premature deaths in 2019 relative to 2013, most of which are associated with emission reductions in the agricultural and industrial sectors.

Embodied Carbon Emissions in Shanghai's Power Industry Based on Input-Output Analysis

Embodied Carbon Emissions in Shanghai's Power Industry Based on Input-Output Analysis

Local carbon emission zone construction in the highly urbanized regions: Application of residential and transport CO2 emissions in Shanghai, China

Optimizing urban morphology can help city managers deploy internal carbon reduction strategies. However, due to the numerous factors that influence urban carbon emissions and the complex paths of their effects, existing research findings are diverse and difficult to compare. This study constructed the local carbon emission zone (LCEZ) which links the highly urbanization morphology profiles and CO2 emission intensity to favor summarizing the form characteristics for the low CO2 emission control in new communities. The approach involved selecting six indicators from the perspectives of urban external morphology, internal characteristics, and development intensity, and using geographically weighted regression (GWR) to identify three key factors. The selected factors were divided into low(1) medium(2) and high(3) categories and were combined with each other to construct the LCEZ. The nonparametric test and GeoDetector model were used to test the difference and similarity of LCEZs, respectively. The results showed that 1) The impacts of functional mixing entropy (FME), building height (BH) and road density (RD) on residential and transport CO2 emissions (RTCE) were relatively large and positive; 2) The low- and medium-FME areas were the major types of LCEZ; 3) The area proportions of the low FME LCEZs in different population areas of the RTCE cold spot regions were the highest (81.84%–88.25%); 4) It is recommended that the morphological feature values of compact-low rise-low network (1-1-1), mid compact-low rise-mid network (1-1-2), compact-mid rise-low network (1-2-1) and mid compact-mid rise-mid network (1-2-2) were taken as the low CO2 emission control of highly urbanized regions.

High-resolution carbon neutrality mapping and a heterogeneity analysis for China's two typical megalopolises

High-resolution carbon neutrality mapping is critical to guide carbon sequestration increase and carbon emissions reduction in spatial. Based on multiple sources of big data, which include both high-precision satellite data and field data, this study explored a new approach to simulate the spatial distribution of carbon neutrality and comprehensively compared the influencing factors of industry structure, land use and landscape for Beijing and Shanghai. The results indicate that carbon emissions in Shanghai are 1.8 times those of Beijing. Carbon sink in Beijing can only offset about 13% of carbon emissions, while Shanghai shows little carbon sink due to limited natural green land. Large areas in the west, north and northwest of Beijing show carbon sink, and high carbon emission values are mainly located in the southeast of the urban area. Shanghai shows a more intensive carbon emissions distribution, and the carbon emissions are widely distributed, covering the whole area except for Chongming island. Carbon emissions in urban regions generally show a decreasing trend from urban centre to suburb. Shanghai has much higher carbon neutrality pressure than Beijing, one reason for this is that Shanghai has very limited natural green land, and another reason is that Shanghai has many more industries, which generate a large amount of carbon emissions, and this amount is still increasing, although industry carbon emissions have obviously decreased since 2000. Landscape can influence carbon emissions but not as strongly as industry and land use structure. The landscape compactness of water, wetland and industry land in Shanghai is higher than Beijing, whereas all the other lands in Beijing are more compact. Finally, some policy implications are proposed for the two cities.

Molecular characteristics of ambient organic aerosols in Shanghai winter before and after the COVID-19 outbreak

During the global pandemic of COVID-19, the world adopted different strategies to avoid the human and economic loss, and so does China. The reduction of human activities during this time period caused reduction in PM emissions. This study adopted a HPLC-Q-TOF-MS to compare the chemical compositions of ambient aerosol samples collected in Shanghai winter before (2018, 2019) and after (2021) the COVID-19 outbreak. The identified compositions were classified into subgroups of CHO, CHN, CHON, CHONS, CHOS and CHN compounds. Results showed that CHO compounds and CHON compounds were dominating the organic compounds in ESI− and ESI+, respectively. The average percentages of CHO− compounds were 57.97 % in 2018, 58.98 % in 2019, and 43.93 % in 2021, respectively. The average percentages of CHON+ compounds were 52.74 % in 2018, 50.34 % in 2019, and 52.02 % in 2021, respectively. The proportion of aliphatic compounds increased gradually during the three years, especially in 2021, indicating that CHO compounds were less affected by aromatic precursors after the COVID-19 outbreak. The contribution of anthropogenic emissions in Shanghai was weakened compared with the previous years. In addition, there was an enhanced emission source containing hydroxyl for CHOS compound formation in 2021. The variations of atmospheric oxidation degree among the three years were not significant.

Trading, storage, or penalty? Uncovering firms' decision-making behavior in the Shanghai emissions trading scheme: Insights from agent-based modeling

The emissions trading scheme (ETS) has become a flagship climatic initiative for regulating greenhouse gas (GHG) emissions. Under an ETS, the emitting firm must simultaneously deal with changing carbon prices and the number of permits and the trade-off between permit trading (if one should buy, sell, or reserve) and permit-consuming production. This study characterizes the different compliance strategies' trade-offs, logic, and options among regulated emitting firms under an ETS. Consequently, this study provides firm-level evidence of regulated firms' strategic responses under the Shanghai ETS, considering detailed firm information, heterogeneous industry characteristics, and ETS architects. The proposed model simulated the trading interactions among diversified firms to portray the firms' decision-making process, and the emergent effects on the ETS market were identified. The results indicate that: 1) the carbon price experiences a non-monotonic “L-shaped” trend, which maintains an initial low level and increases sharply after crossing a threshold; 2) with increasing carbon prices, the trading in ETS becomes more active, especially among the low-emission firms; 3) the current ETS penalty is too limited and generic, which inadequately induces technological development and carbon reduction among firms. Finally, policy suggestions are provided for future optimization of the ETS mechanism. Overall, this study contributes to operation management literature by evaluating decision-making behaviors in a dynamic environment. Our findings have global implications for policymakers and managers in the private sector.

Forecast of Transportation CO2 Emissions in Shanghai under Multiple Scenarios

A reduction in CO2 emissions from transportation is of great significance to achieve the goal of “peak carbon and carbon neutrality” in China. For 2003–2019, this paper calculates the transportation CO2 emissions in Shanghai and constructs an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model for forecasting. The result shows that from 2003 to 2019, total and per capita CO2 emissions from Shanghai’s transportation sector increased, but the rate of growth decreased. Oil consumption was the main source of emissions, accounting for more than 92%. The study extended the STIRPAT model to analyze the driving factors for emissions. It shows that population size, passenger turnover, per capita GDP, transportation intensity, and energy intensity are positively correlated with emissions. Energy structure (the proportion of clean energy) has a negative impact, restraining growth. Under multiple scenarios, the forecast shows that Shanghai’s transportation sector can reach a CO2 emissions peak before 2030. However, overgrowth of the transportation sector should be avoided. Progress in green and low-carbon technology is particularly important to achieve China’s peak carbon goal. Shanghai should actively build an efficient green transportation system, continue to optimize the transportation energy structure, and promote green and low-carbon travel for residents.

Analyzing greenhouse gas emissions from municipal wastewater treatment plants using pollutants parameter normalizing method：a case study of Beijing

Wastewater treatment plants (WWTPs) contribute increasing amounts of greenhouse gases (GHGs), due to rapid development and increasingly stringent wastewater discharge limits in China. In this study, GHG emissions from 38 WWTPs in Beijing were estimated using the pollutants parameter normalizing method (PPNM) and the effects of various factors were analyzed. The result showed that, the total GHG emissions of Beijing WWTPs in 2017 were 1 045 661.5 t CO2-eq (tons of CO2-equivalent), the direct and indirect emissions were 186 366.0 and 859 295.5 t CO2-eq, respectively. The average emission intensity was 0.603 kg CO2-eq/m3. Direct and indirect GHG emissions were influenced by various factors, such as treatment process, influent parameters and treatment scale. The anaerobic-anoxic-oxic (AAO) process demonstrated low emission intensities (0.092 kg CO2/m3), while biofilm processes (MBBR) obtained relatively high emission intensities (0.277 kg CO2/m3). Direct GHG emission has a positive correlation with the amount of pollutant removed and low influent pollutant concentrations (chemical oxygen demand [COD]<300 mg/L and total nitrogen [TN] <20 mg/L) can lead to high indirect GHG emission intensities. The low power utilization efficiency of small-scale WWTPs (<1 × 107 t/a; 6.60 kWh/kg COD; 30.54 kWh/kg TN) contributed to additional GHG emissions. Compared with the GHG emissions in Shanghai in 2016, higher influent pollutant concentrations and stricter discharge limits contributed to higher GHG emission intensity in Beijing for both direct (0.108 kg CO2/m3) and indirect (0.496 kg CO2/m3) emissions. The comparison results between cities indicated that the PPNM method achieved a more accurate account of GHG emissions, and the analysis results provided support for carbon emission reduction.

Identification and Characterization of PM2.5 Emission Sources in Shanghai during COVID-19 Pandemic in the Winter of 2020

The novel coronavirus (COVID-19) epidemic broke out in Wuhan at the end of 2019 and spread around the whole of China in 2020. In order to reduce the spread of COVID-19, transportation and industrial activities in different regions were limited to varying degrees. This study uses bivariate concentration polar plots, integrated with k-means clustering and temporal variation analyses for PM2.5 time series data, to understand the PM2.5 source characteristics in Shanghai during the COVID-19 pandemic in the winter of 2020. Our findings show that 34.33% of the PM2.5 particles arise from external sources while 65.67% are from local sources. The results of source apportionment combined with land use, wind speed, and direction data are further used to locate the most likely directions of different source categories and geographic origins of PM2.5. During the lockdown period in 2020, traffic and industrial activity were still primary local sources of PM2.5 emissions in Shanghai. The growth of motor vehicle ownership, limited public transport, and a large volume of freight transport in Shanghai result in a higher level of PM2.5 concentrations on weekends than in midweeks. On the other hand, the regional-scale transport of air pollutants from the Yangtze River Delta, the Central Plains, the inland area of northern China, and coastal cities in the north and south of Shanghai aggravates PM2.5 pollution in Shanghai under unfavorable meteorological conditions. The methods and results presented here lay a basis for further study on the complicated effects of meteorological and anthropogenic factors on PM2.5 pollution and on the development of detailed and urgent strategies for the improvement of air quality.

Effects of energy structure differences on chemical compositions and respiratory health of PM2.5 during late autumn and winter in China

To understand the influence of the energy structure (including solid fuel and clean energy) on air pollution, two comprehensive measurement campaigns were conducted in Baoding and Shanghai in late autumn and winter during 2017–2018. The chemical compositions, driving factors, regional transport of pollutants, and potential respiratory disease (RD) health risks of PM2.5 for Baoding and Shanghai were analyzed. The results showed that the concentration of PM2.5 in Baoding (156.9 ± 139.8 μg m−3) was 2.6 times of that in Shanghai (60.9 ± 45.9 μg m−3). The most important contributor to PM2.5 in Baoding was organic matter (OM), while inorganic aerosols accounted for major fractions of PM2.5 in Shanghai. Positive matrix factorization (PMF) results indicated that coal combustion (CC; 39%) accounted for the most in Baoding, followed by secondary aerosols (21%), biomass burning (BB; 20%), industrial emissions (14%), dust (3%), and vehicle exhaust (2%). However, the average contribution in Shanghai followed the order: secondary aerosols (44%), vehicle exhaust (36%), dust (11%), marine aerosols (6%), and BB (3%). The evolution of source contributions at different pollution levels revealed that haze episodes in Baoding and Shanghai were triggered by CC and secondary formation, respectively; however, the air quality on clean days in Baoding and Shanghai was affected mostly by BB and vehicle emissions, respectively. Potential source contribution function (PSCF) results suggested that CC in Baoding was primarily from local emissions, while BB was primarily from local and regional transport. Vehicle exhaust and secondary aerosols in Shanghai were mainly from local emissions and regional transport. The number of RD deaths related to haze episodes in Baoding and Shanghai were 215 (95% CI: 109, 319) and 76 (95% CI: 11, 135), respectively. This research also emphasized the importance of further attention to the usage of coal in Baoding and vehicle emissions in Shanghai.

Trading, Storage, or Penalty? Uncovering Firms’ Decision-Making Behavior in the Shanghai Emissions Trading Scheme: Insights from Agent-Based Modeling

Trading, Storage, or Penalty? Uncovering Firms’ Decision-Making Behavior in the Shanghai Emissions Trading Scheme: Insights from Agent-Based Modeling

Characteristics and Control Strategies on Summertime Peak Ozone Concentration in Shanghai

With the continuous development of air pollution control measures, the concentration of PM2.5 in Shanghai has shown a conspicuous downward trend in recent years. However, frequent O3 pollution events have highlighted the urgent need to explore the occurrence patterns of O3 pollution and develop scientific strategies for reducing O3 peaks. This study examines data from July 2017, when the cumulative number of O3 pollution days in 17 cities in the Yangtze River Delta was 165 days, of which Shanghai was the most serious, with an exceedance rate of 64.5%. During this period, the average concentration of NO2 in Shanghai was 27.1 μg·m-3 and volatile organic copunds (VOCs) mixing ratio was 22.5×10-9. By analyzing ozone precursor concentrations and meteorological factors, we determined that these events mainly resulted from a combination of unfavorable meteorological conditions such as high temperature, low humidity, low wind speed, and high precursor emissions. WRF-CMAQ scenario simulations showed that a reduction in precursor emissions in Shanghai alone would have a limited controlling effect on regional O3 pollution. Thus, regional joint control is recommended when widespread pollution events occur. Our analysis shows that if VOCs in Shanghai and nine neighboring cities can be reduced by 30%, the maximum 8-h O3 concentration in Shanghai could be reduced by 7.2%. If the reduction number of these cities rises to 17, the maximum 8-h O3 concentration reduction rate in Shanghai will increase to 7.8%. It is also recommended that the VOCs:NOx reduction ratio should be strictly controlled at more than 3:1, or else the O3 concentration in some areas will increase.

Research on urban density model and resilient development model under the guidance of carbon emission

In this study, we used time series data from 2001 to 2018 to quantify the relationship between urban density and carbon emissions in Shanghai. The regression equation is used to analyze the relationship between urban carbon emissions and urban density in the past ten years. According to national policies and the calculation of peak carbon emissions, the urban density under the peak carbon emissions is derived as the appropriate urban density. This paper puts forward the concept of urban density index, calculates the density index of Shanghai and analyzes it.

The Potential Impacts of Electric Vehicles on Urban Air Quality in Shanghai City

The Shanghai government has outlined plans for the new vehicles used for the public transportation, rental, sanitation, postal, and intra-city freight to be completely powered by electricity by 2020. This paper analyzed the characteristics of vehicle emissions in Shanghai in the past five years. The potential reduction in road traffic related emissions due to the promotion and application of electric vehicle in Shanghai was evaluated. The potential reduction was quantified by vehicular emissions. The vehicular emissions inventories are calculated by the COPERT IV model under the different scenarios, of which the results indicate that promoting electric vehicles is the efficient measure to control all road traffic related emissions and improve urban air quality. The results also provided basis and support for making policies to promote and manage electric vehicles.

上海城市森林植被固碳功能及其抵消能源碳排放效果评估

植被固碳功能是城市森林的重要生态服务之一，但是定量揭示城市森林植被固碳功能空间格局及其抵消碳排放的研究较少。基于光合速率法和生物量法测算了上海城市森林植被的固定CO₂功能，并结合其空间分布格局与区域CO₂排放状况对比分析了抵消能源CO₂排放的成效。结果表明，2017年上海城市森林植被可固定CO₂ 135.57万t，约合单位面积固碳17.02 t/hm²。上海城市森林植被的固定CO₂功能呈现出"中间低、四周高"的空间格局，崇明区和浦东新区的城市森林植被提供了近52.8%的固定CO₂效益。2017年上海市能源消费的CO₂排放量高达2.73亿t，且呈现出"中间高、四周低"空间分布特征，中心城区能源CO₂排放约占11.49%。整体来看，上海城市森林植被可抵消0.50%的能源CO₂排放，但植被固定CO₂功能与能源CO₂排放的空间匹配较差，中心城区城市森林植被的CO₂抵消率仅为0.08%。因此，重点提升中心城区森林植被的固碳功能并加强海洋碳吸收功能的监测与利用尤其重要。;Accompanied by the expansion of cities worldwide and the massive use of fossil fuels, ever-increasing carbon dioxide (CO₂) emissions in the urban areas contributes to global climate change. Studies from urban forest have reported that quantification of forest biomass and photosynthetic rate are of great significance to forest carbon cycle studies and climate-change mitigation. However, most former studies have focused on calculating CO₂ sequestration of urban forests at regional and urban scale, and few reports are available in the spatial differences of carbon fixation and the efficacy on offsetting energy CO₂ emissions in urban areas at finer scale. Therefore, based on field survey data and urban forests data derived from high-resolution Gaofen No.2 images, this paper modeled the spatial changes of CO₂ fixation by using assimilation method and biomass method in Shanghai, the most prosperous metropolitan area in China. We then mapped CO₂ emission in pixel size based on yearbook data, population distribution and NPP-VⅡRS nighttime light data. The results showed that the urban forest in Shanghai could annually absorb 1.36 million tons of CO₂, and the average CO₂ fixation capacity reached 17.02 tons per hectare. The CO₂ fixation capacity varied among different urban forest types. The broadleaved forest ranked first (18.22 t/hm²) while the conifer forest manifested the lowest CO₂ fixation capacity (7.11 t/hm²). The spatial distribution of CO₂ fixation presented a significantly uneven overall spatial distribution: low in the middle and high on all sides, and the forest in Chongming District and Pudong New District jointly contributed 52.8% of the total sequestrated CO₂. In addition, the results indicated that the amount of CO₂ emission from annual energy consumption reached 0.27 billion tons in 2017 and the amount of CO₂ emission from center area occupied 11.49% of the total CO₂ emission, being spatially high at center and low at fringe. Collectively, the annual CO₂ sequestration of urban forest could only offset 0.5% of the annual energy CO₂ emissions in Shanghai. Furthermore, the CO₂ sequestration spatially mismatched the CO₂ emission. Only 0.08% of energy CO₂ emission in the center area could be offset by its urban forest. Therefore, these results can be used to help assess the actual role of urban forests in reducing atmospheric CO₂ in Shanghai, and we suggested Shanghai should promote the carbon dioxide fixation capacity of urban forests in the center area, and strengthen the observation and utilization of carbon dioxide sequestration in the marine ecosystem.

Characteristics and source apportionment of non-polar organic compounds in PM2.5 from the three megacities in Yangtze River Delta region, China

Fine particulate matter samples (PM2.5) were collected from the three megacities of the Yangtze River Delta (i.e., Shanghai, Hangzhou, and Nanjing) during November 11 to December 6, 2016. Organic carbon (OC), elemental carbon (EC), and four types of non-polar organic compounds such as polycyclic aromatic hydrocarbons (PAHs), n-alkanes, hopanes and steranes were analyzed in the samples. The average PM2.5 mass concentrations were 47.0, 64.2 and 45.5 μg m−3 in Shanghai, Hangzhou and Nanjing, respectively. OC and EC concentrations were 4.47 and 0.78 μg m−3, 15.38 and 2.97 μg m−3, 10.35 and 2.06 μg m−3, respectively. n-Alkanes accounted for the largest abundance of the identified non-polar organic compounds, followed by PAHs, hopanes and steranes. The positive matrix factorization (PMF) analysis demonstrated that vehicular emissions, coal burning and industrial emissions were the most important sources. Vehicle exhaust was the major source of emissions in Shanghai (61.0%) and Hangzhou (45.3%), whereas coal combustion emissions was dominant (48.8%) in Nanjing. In addition, the estimated lifetime cancer risk (ILCR) values exposed to PAHs for adults (6.54 × 10−5) and children (7.32 × 10−6) suggested that PAHs levels in this region pose potential health risks. Combining the health risk model with PMF further concluded that vehicular emissions (49.0%) and coal burning (35.7%) were the two major sources that contribute to the ILCR values in these three cities.

Port-Related Emissions, Environmental Impacts and Their Implication on Green Traffic Policy in Shanghai

The port of Shanghai, as the world’s largest container port, has been experiencing rapid development in recent years, with increasing cargo throughput capacity. The combustion of diesel fuels used by internal and external port-related container trucks and in-port machineries can release various pollutants, causing air pollution. The terminals are close to the residential area, and the emissions are concentrated, which is worth paying attention to. This study aims to synthetically assess the port-related emissions and their environmental impacts. We firstly constructed an emission inventory of air pollutants in the port of Shanghai and then used the WRF-CMAQ model to estimate the influence of port-related source emissions on air quality. The results show that the annual emissions of SO2, NOX, CO, VOCS, PM, PM10, PM2.5, CO2, BC and OC caused by cargo-handling equipment were 21.88 t, 1811.22 t, 1741.72 t, 222.76 t, 61.52 t, 61.42 t, 58.41 t, 141,805.40 t, 26.80 t and 10.07 t in 2015. The emissions of NOX, CO, VOCS, PM10 and PM2.5 caused by external port-related container trucks were 18,002.92 t, 5308.0 t, 1134.57 t, 711.12 t and 640.58 t. The exhaust of external port-related container trucks was much larger than that of cargo-handling equipment, so the impact on air quality was also higher than that of the machinery. The peak annual average concentrations of PM2.5 and NOX contributed by the port-related sources were 1.75 μg/m3 and 49.21 μg/m3, respectively, which accounted for 3.08% and 36.7%, respectively, of the simulated ambient concentrations by all the anthropogenic emissions in Shanghai. Our results imply that the emission control policy to reduce the combined port-related emissions, especially for the cargo-delivery transportation phase from port to city, is key for large coastal port cities such as Shanghai.

Metro Proximity and Built Environment on Commuting CO&lt;sub&gt;2&lt;/sub&gt; Emissions in Shanghai

Metro Proximity and Built Environment on Commuting CO&lt;sub&gt;2&lt;/sub&gt; Emissions in Shanghai

Community Structure and Influencing Factors of Airborne Microbial Aerosols over Three Chinese Cities with Contrasting Social-Economic Levels

As an important part of atmospheric aerosol, airborne bacteria have major impacts on human health. However, variations of airborne community structure due to human-induced activities and their possible impact on human health have not been well understood. In this study, we sampled atmospheric microbial aerosols in three Chinese cities (Shanghai, Xiamen, and Zhangzhou) with contrasting social-economic levels and analyzed the bacterial composition using high-throughput sequencing methods. A high similarity of the predominant phyla was observed in three cities but the relative abundances were quite different. At the genus level, the most dominant genus in Shanghai and Xiamen was Deinococcus while the most dominant genus in Zhangzhou was Clostridium. The different characteristics of airborne bacterial in the three cities above may be ascribed to the environmental variables affected by human over-activities such as the vehicle exhausts and coal-burning emissions in Shanghai, the tourist aggregation and construction works in Xiamen, the extensive uses of chemical fertilizers, and agricultural activities in Zhangzhou. The variation of the bacterial community and the pathogenic bacteria detected in three cities would have a potential threat to human health.