Quantification and Characterization of Fine Plastic Particles as Considerable Components in Atmospheric Fine Particles.

The negative effects of air pollution, especially fine particulate matter (PM2.5, particles with an aerodynamic diameter of ≤2.5 µm), on human health, climate, and ecosystems are causing significant concern. Nevertheless, little is known about the contributions of emerging pollutants such as plastic particles to PM2.5 due to the lack of continuous measurements and characterization methods for atmospheric plastic particles. Here, we investigated the levels of fine plastic particles (FPPs) in PM2.5 collected in urban Shanghai at a 2 h resolution by using a novel versatile aerosol concentration enrichment system that concentrates ambient aerosols up to 10-fold. The FPPs were analyzed offline using the combination of spectroscopic and microscopic techniques that distinguished FPPs from other carbon-containing particles. The average FPP concentrations of 5.6 µg/m3 were observed, and the ratio of FPPs to PM2.5 was 13.2% in this study. The FPP sources were closely related to anthropogenic activities, which pose a potential threat to ecosystems and human health. Given the dramatic increase in plastic production over the past 70 years, this study calls for better quantification and control of FPP pollution in the atmosphere.

Abundant nitrogenous secondary organic aerosol formation accelerated by cloud processing.

Nitrogenous organic (CHON), crucial for secondary organic aerosol (SOA), forms through poorly studied mechanisms in clouds. Our study explores CHON transformation during cloud processes (CPs). These processes play a vital role in enhancing the variety of CHONs, leading to the formation of CHONs with oxygen atom counts ranging from 1 to 10 and double bond equivalent (DBE) values spanning from 2 to 10. We proposed that the CHONs formed during CPs are formed through aqueous phase reactions with CHO compound precursors via nucleophilic attacks by NH3. This scheme can be account for roughly three-quarters of the CHONs by number in cloud water, and near two-thirds of all CHONs are formed through reactions between NH3 and carbonyl-containing biogenic volatile organic compound (BVOC) ozonolysis intermediates. This study provides the first insights into the evolution of CHONs during CPs and reveals the significant roles of CPs in the formation of CHONs.

Photochemistry in the urban agglomeration along the coastline of southeastern China: Pollution mechanism and control implication.

The concentrations of ground-level ozone (O3) in China have undergone a rapid increase in recent years, resulting in adverse impacts on the air quality and climate change. However, limited research has been conducted on the coastal urban agglomerations with increasingly serious O3 pollution. Therefore, in order to better understand in situ photochemistry, comprehensive field observations of O3 and its precursors, coupled with the model simulation, were conducted in autumn of 2019 at six sites in an urban agglomeration along the coastline of southeastern China. Results indicated that O3 pollution in the southern part of the urban agglomeration was more severe than that in the northern part, due to higher levels of O3 precursors and stronger atmospheric oxidation capacity (AOC) in the southern regions. Oxygenated volatile organic compounds (OVOCs), NO2, and CO dominated the total OH reactivity, and the site-average daytime Ox (O3 + NO2) increments correlated well (R2 = 0.94) with the total OH reactivity of CO and VOCs at these sites except for Quanzhou, where industrial emissions (35.1 %) and solvent usages (33.7 %) dominated the VOC sources. However, vehicle exhausts (31.1 %) were the most predominant contributors to the VOC sources at other sites. The results of model simulations showed that net O3 formation rates were larger at the southern sites. Furthermore, O3 production was mainly controlled by VOCs at most sites, but co-limited by VOCs and NOx at Quanzhou. The most significant VOC groups contributing to O3 formation were aromatics and alkenes, with m/p-xylene, toluene, propene, and ethene being the main contributors at these sites. This study offers a more comprehensive understanding of the characteristics and formation of photochemical pollutions on the scale of the urban areas, indicating the critical need to reduce VOC emissions as a means of mitigating their photochemical effects.

Widespread missing super-emitters of nitrogen oxides across China inferred from year-round satellite observations.

Nitrogen oxides (NOx ≡ NO + NO2) play a central role in air pollution and are targeted for emission mitigation by environmental protection agencies globally. Unique challenges for mitigation are presented by super-emitters, typically with the potential to dominate localized NOx budgets. Nevertheless, identifying super-emitters still challenges emission mitigation, while the spatial resolution of emission monitoring rises continuously. Here we develop an efficient, super-resolution (1 × 1 km2) inverse model based on year-round TROPOMI satellite observations over China. Consequently, we resolve hundreds of super-emitters in virtually every corner of China, even in remote and mountainous areas. They are attributed to individual plants or parks, mostly associated with industrial sectors, like energy, petrochemical, and iron and steel industries. State-of-the-art bottom-up emission estimates (i.e., MEICv1.3 and HTAPv2), as well as classic top-down inverse methods (e.g., a CTM coupled with the Ensemble Kalman Filter), do not adequately identify these super-emitters. Remarkably, more than one hundred super-emitters are unambiguously missed, while the establishments or discontinuations of the super-emitters potentially lead to under- or over-estimates, respectively. Moreover, evidence shows that these super-emitters generally dominate the NOx budget in a localized area (e.g., equivalent to a spatial scale of a medium-sized county). Although our dataset is incomplete nationwide due to the undetectable super-emitters on top of high pollution, our results imply that super-emitters contribute significantly to national NOx budgets and thus suggest the necessity to address the NOx budget by revisiting super-emitters on a large scale. Integrating the results we obtain here with a multi-tiered observation system can lead to identification and mitigation of anomalous NOx emissions.

Quantifying on-road vehicle emissions during traffic congestion using updated emission factors of light-duty gasoline vehicles and real-world traffic monitoring big data.

Light-duty gasoline vehicles (LDGVs) have made up >90 % of vehicle fleets in China since 2019, moreover, with a high annual growth rate (> 10 %) since 2017. Hence, accurate estimates of air pollutant emissions of these fast-changing LDGVs are vital for air quality management, human healthcare, and ecological protection. However, this issue is poorly quantified due to insufficient reserves of timely updated LDGV emission factors, which are dependent on real-world activity levels. Here we constructed a big dataset of explicit emission profiles (e.g., emission factors and accumulated mileages) for 159,051 LDGVs based on an official I/M database by matching real-time traffic dynamics via real-world traffic monitoring (e.g., traffic volumes and speeds). Consequently, we provide robust evidence that the emission factors of these LDGVs follow a clear heavy-tailed distribution. The top 10 % emitters contributed >60 % to the total fleet emissions, while the bottom 50 % contributed <10 %. Such emission factors were effectively reduced by 75.7-86.2 % as official emission standards upgraded gradually (i.e., from China 2 to China 5) within 13 years from 2004 to 2017. Nevertheless, such achievements would be offset once traffic congestion occurred. In the real world, the typical traffic congestions (i.e., vehicle speed <5 km/h) can lead to emissions 5- 9 times higher than those on non-congested roads (i.e., vehicle speed >50 km/h). These empirical analyses enabled us to propose future traffic scenarios that could harmonize emission standards and traffic congestion. Practical approaches on vehicle emission controls under realistic conditions are proposed, which would provide new insights for future urban vehicle emission management.

Rapid assessments of light-duty gasoline vehicle emissions using on-road remote sensing and machine learning.

In-time and accurate assessments of on-road vehicle emissions play a central role in urban air quality and health policymaking. However, official insight is hampered by the Inspection/Maintenance (I/M) procedure conducted in the laboratory annually. It not only has a large gap to real-world situations (e.g., meteorological conditions) but also is incapable of regular supervision. Here we build a unique dataset including 103,831 light-duty gasoline vehicles, in which on-road remote sensing (ORRS) measurements are linked to the I/M records based on the vehicle identification numbers and license plates. On this basis, we develop an ensemble model framework that integrates three machining learning algorithms, including neural network (NN), extreme gradient boosting (XGBoost), and random forest (RF). We demonstrate that this ensemble model could rapidly assess the vehicle-specific emissions (i.e., CO, HC, and NO). In particular, the model performs quite well for the passing vehicles under normal conditions (i.e., lower VSP (<18 kw/t), temperature (6-32 °C), relative humidity (<80%), and wind speed (<5 m/s)). Together with the current emission standard, we identify a large number of the 'dirty' (2.33%) or 'clean' (74.92%) vehicles in the real world. Our results show that the ORRS measurements, assisted by the machine-learning-based ensemble model developed here, can realize day-to-day supervision of on-road vehicle-specific emissions. This approach framework provides a valuable opportunity to reform the I/M procedures globally and mitigate urban air pollution deeply.

Non-stop industries were the main source of air pollution during the 2020 coronavirus lockdown in the North China Plain.

Despite large decreases of emissions of air pollution during the coronavirus disease 2019 (COVID-19) lockdown in 2020, an unexpected regional severe haze has still occurred over the North China Plain. To clarify the origin of this pollution, we studied air concentrations of fine particulate matter (PM2.5), NO2, O3, PM10, SO2, and CO in Beijing, Hengshui and Baoding during the lockdown period from January 24 to 29, 2020. Variations of PM2.5 composition in inorganic ions, elemental carbon and organic matter were also investigated. The HYSPLIT model was used to calculate backward trajectories and concentration weighted trajectories. Results of the cluster trajectory analysis and model simulations show that the severe haze was caused mainly by the emissions of northeastern non-stopping industries located in Inner Mongolia, Liaoning, Hebei, and Tianjin. In Beijing, Hengshui and Baoding, the mixing layer heights were about 30% lower and the maximum relative humidity was 83% higher than the annual averages, and the average wind speeds were lower than 1.5 m s-1. The concentrations of NO3 -, SO4 2-, NH4 +, organics and K+ were the main components of PM2.5 in Beijing and Hengshui, while organics, K+, NO3 -, SO4 2-, and NH4 + were the main components of PM2.5 in Baoding. Contrary to previous reports suggesting a southerly transport of air pollution, we found that northeast transport caused the haze formation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10311-021-01314-8.

City-level air quality improvement in the Beijing-Tianjin-Hebei region from 2016/17 to 2017/18 heating seasons: Attributions and process analysis.

With the implementation of clean air strategies, PM2.5 pollution abatement has been observed in the "2 + 26" cities in the Beijing-Tianjin-Hebei (BTH) region (referred to as the BTH2+26) and their surrounding areas. To identify the drivers for PM2.5 concentration decreases in the BTH2+26 cites from the 2016/17 heating season (HS1617) to the 2017/18 heating season (HS1718), we investigated the contributions of meteorological conditions and emission-reduction measures by Community Multi-Scale Air Quality (CMAQ) model simulations. The source apportionments of five sector sources (i.e., agriculture, industry, power plants, traffic and residential), and regional sources (i.e., local, within-BTH: other cities within the BTH2+26 cities, outside-BTH, and boundary conditions (BCON)) to the PM2.5 decreases in the BTH2+26 cities were estimated with the Integrated Source Apportionment Method (ISAM). Mean PM2.5 concentrations in the BTH2+26 cities substantially decreased from 77.4 to 152.5 µg m-3 in HS1617 to 52.9-101.9 µg m-3 in HS1718, with the numbers of heavy haze (daily PM2.5 ≥150 µg m-3) days decreasing from 17-77 to 5-30 days. The model simulation results indicated that the PM2.5 concentration decreases in most of the BTH2+26 cities were attributed to emission reductions (0.4-55.0 µg m-3, 2.3-81.6% of total), but the favorable meteorological conditions also played important roles (1.9-25.4 µg m-3, 18.4-97.7%). Residential sources dominated the PM2.5 reductions, leading to decreases in average PM2.5 concentrations by more than 30 µg m-3 in severely polluted cities (i.e., Shijiazhuang, Baoding, Xingtai, and Beijing). Regional source analyses showed that both local and within-BTH sources were significant contributors to PM2.5 concentrations for most cities. Emission controls in local and within-BTH sources in HS1718 decreased the average PM2.5 concentrations by 0.1-47.2 µg m-3 and 0.3-22.1 µg m-3, respectively, relative to those in HS1617. Here we demonstrate that a combination of favorable meteorological conditions and anthropogenic emission reductions contributed to the improvement of air quality from HS1617 to HS1718 in the BTH2+26 cities.

A comparative study of two-way and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US: performance evaluation and impacts of chemistry-meteorology feedbacks on air quality.

The two-way coupled Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) model has been developed to more realistically represent the atmosphere by accounting for complex chemistry-meteorology feedbacks. In this study, we present a comparative analysis of two-way (with consideration of both aerosol direct and indirect effects) and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US. Long-term (5 years from 2008 to 2012) simulations using WRF-CMAQ with both offline and two-way coupling modes are carried out with anthropogenic emissions based on multiple years of the U.S. National Emission Inventory and chemical initial and boundary conditions derived from an advanced Earth system model (i.e., a modified version of the Community Earth System Model/Community Atmospheric Model). The comprehensive model evaluations show that both two-way WRF-CMAQ and WRF-only simulations perform well for major meteorological variables such as temperature at 2 m, relative humidity at 2 m, wind speed at 10 m, precipitation (except for against the National Climatic Data Center data), and shortwave and longwave radiation. Both two-way and offline CMAQ also show good performance for ozone (O3) and fine particulate matter (PM2.5). Due to the consideration of aerosol direct and indirect effects, two-way WRF-CMAQ shows improved performance over offline coupled WRF and CMAQ in terms of spatiotemporal distributions and statistics, especially for radiation, cloud forcing, O3, sulfate, nitrate, ammonium, elemental carbon, tropospheric O3 residual, and column nitrogen dioxide (NO2). For example, the mean biases have been reduced by more than 10 W m-2 for shortwave radiation and cloud radiative forcing and by more than 2 ppb for max 8 h O3. However, relatively large biases still exist for cloud predictions, some PM2.5 species, and PM10 that warrant follow-up studies to better understand those issues. The impacts of chemistry-meteorological feedbacks are found to play important roles in affecting regional air quality in the US by reducing domain-average concentrations of carbon monoxide (CO), O3, nitrogen oxide (NO x ), volatile organic compounds (VOCs), and PM2.5 by 3.1% (up to 27.8%), 4.2% (up to 16.2%), 6.6% (up to 50.9%), 5.8% (up to 46.6%), and 8.6% (up to 49.1%), respectively, mainly due to reduced radiation, temperature, and wind speed. The overall performance of the two-way coupled WRF-CMAQ model achieved in this work is generally good or satisfactory and the improved performance for two-way coupled WRF-CMAQ should be considered along with other factors in developing future model applications to inform policy making.

Unexpected rise of ozone in urban and rural areas, and sulfur dioxide in rural areas during the coronavirus city lockdown in Hangzhou, China: implications for air quality.

The outbreak of coronavirus named COVID-19, initially identified in Wuhan, China in December 2019, has spread rapidly at the global scale. Most countries have rapidly stopped almost all activities including industry, services and transportation of goods and people, thus decreasing air pollution in an unprecedented way, and providing a unique opportunity to study air pollutants. While satellite data have provided visual evidence for the global reduction in air pollution such as nitrogen dioxide (NO2) worldwide, precise and quantitative information is missing at the local scale. Here we studied changes in particulate matter (PM2.5, PM10), carbon monoxide (CO), NO2, sulfur dioxide (SO2) and ozone (O3) at 10 urban sites in Hangzhou, a city of 7.03 million inhabitants, and at 1 rural site, before city lockdown, January 1-23, during city lockdown, January 24-February 15, and during resumption, February 16-28, in 2020. Results show that city lockdown induced a sharp decrease in PM2.5, PM10, CO, and NO2 concentrations at both urban and rural sites. The NO2 decrease is explained by reduction in traffic emissions in the urban areas, and by lower regional transport in rural areas during lockdown, as expected. SO2 concentrations decreased from 6.3 to 5.3 µg m-3 in the city, but increased surprisingly from 4.7 to 5.8 µg m-3 at the rural site: this increase is attributed both to higher coal consumption for heating and emissions from traditional fireworks of the Spring Eve and Lantern Festivals during lockdown. Unexpectedly, O3 concentrations increased by 145% from 24.6 to 60.6 µg m-3 in the urban area, and from 42.0 to 62.9 µg m-3 in the rural area during the lockdown. This finding is explained by the weakening of chemical titration of O3 by NO due to reductions of NOx fresh emissions during the non-photochemical reaction period from 20:00 PM to 9:00 AM (local time). During the lockdown, compared to the same period in 2019, the daily average concentrations in the city decreased by 42.7% for PM2.5, 47.9% for PM10, 28.6% for SO2, 22.3% for CO and 58.4% for NO2, which is obviously explained by the absence of city activities. Overall, we observed not only the expected reduction in some atmospheric pollutants (PM, SO2, CO, NO2), but also unexpected increases in SO2 in the rural areas and of ozone (O3) in both urban and rural areas, the latter being paradoxically due to the reduction in nitrogen oxide levels. In other words, the city lockdown has improved air quality by reducing PM2.5, PM10, CO, and NO2, but has also decreased air quality by augmenting O3 and SO2.

The characteristics of air pollution induced by the quasi-stationary front: Formation processes and influencing factors.

The quasi-stationary front is a significant weather system which influences East Asia in spring. The air quality deteriorated along with the moist circumstance when the quasi stationary front dominated the area. Surface meteorological parameters, air pollutants and PM2.5 chemical species were observed during the air pollution episode. Liquid water content and aerosol acidity were calculated by thermodynamic model in order to investigate heterogeneous/aqueous reactions for secondary aerosol formation. The episode was divided into four stages based on quasi-stationary front influences. Hourly PM2.5 concentrations were up to 150.2 µg·m-3 while O3 concentrations reached the minimum value of 1.27 µg·m-3, indicating that the precursor gas NOx participated in the different reactions during the episode. Nitrate proportion of water-soluble inorganic ions was 42.2%. High concentrations of secondary inorganic aerosol ions and the high sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR) indicated the increasing conversions from SO2 and NOx to their corresponding particulate phases. Ratios of [NO3-]/[SO42-] and [NH4+]/[SO42-] in the four stages declared that nitrate formation preferred heterogeneous conversions. A series of liquid water content (LWC) fitting equations between relative humidity and inorganic ions were conducted to verify heterogeneous aqueous reactions of NO2 and secondary nitrate formation. The results of this study highlighted the significance of LWC and chemical reactions associated with acidity during the specific synoptic situation in South China.

[Source Apportionment of Air Pollutants for a Typical Pollution Event in Zhaoqing].

Based on observational data for pollutants and meteorology, this study analyzed the pollution episode that occurred during Dec 17th to 23th in 2018 in Zhaoqing, Guangdong Province, China. Using the source apportionment model CMAQ-ISAM and the hybrid receptor model, the regional contributions to air pollution were examined. The results showed that low-pressure conditions had an adverse effect on the diffusion of pollutants during this pollution episode in Zhaoqing. Prior to the pollution episode, pollutants were mainly derived from Zhaoqing and Qingyuan, accounting for 19.2% and 10.7% of pollutants, respectively. As well as pollutants from Guangdong Province, long-distance transport of pollutants from Jiangxi, Hunan, Hubei, and Shaanxi accounted for approximately 64.5% of the total during the non-pollution period. During the polluted episode, major cities in Pearl River Delta and the eastern part of Guangdong Province contributed more pollutants as a surface high-pressure field moved southward. Zhaoqing, Foshan, Dongguan, Guangzhou, and Huizhou contributed 25.5%, 14.8%, 9.8%, 9.5%, and 5.3% of the pollutants, respectively. Cities in the eastern part of Guangdong Province including Heyuan, Meizhou, Shanwei, Jieyang, Shantou, and Chaozhou contributed 13.7% of the total pollutants. In addition, pollutants from Fujian, Jiangxi, and the Yangtze River Delta accounted for approximately 32.9%. Furthermore, pollutants transported under marine influences were one of the main causes of this pollution episode in Zhaoqing.

Aerosol-driven droplet concentrations dominate coverage and water of oceanic low-level clouds.

A lack of reliable estimates of cloud condensation nuclei (CCN) aerosols over oceans has severely limited our ability to quantify their effects on cloud properties and extent of cooling by reflecting solar radiation-a key uncertainty in anthropogenic climate forcing. We introduce a methodology for ascribing cloud properties to CCN and isolating the aerosol effects from meteorological effects. Its application showed that for a given meteorology, CCN explains three-fourths of the variability in the radiative cooling effect of clouds, mainly through affecting shallow cloud cover and water path. This reveals a much greater sensitivity of cloud radiative forcing to CCN than previously reported, which means too much cooling if incorporated into present climate models. This suggests the existence of compensating aerosol warming effects yet to be discovered, possibly through deep clouds.

Mitigation of severe urban haze pollution by a precision air pollution control approach.

Severe and persistent haze pollution involving fine particulate matter (PM2.5) concentrations reaching unprecedentedly high levels across many cities in China poses a serious threat to human health. Although mandatory temporary cessation of most urban and surrounding emission sources is an effective, but costly, short-term measure to abate air pollution, development of long-term crisis response measures remains a challenge, especially for curbing severe urban haze events on a regular basis. Here we introduce and evaluate a novel precision air pollution control approach (PAPCA) to mitigate severe urban haze events. The approach involves combining predictions of high PM2.5 concentrations, with a hybrid trajectory-receptor model and a comprehensive 3-D atmospheric model, to pinpoint the origins of emissions leading to such events and to optimize emission controls. Results of the PAPCA application to five severe haze episodes in major urban areas in China suggest that this strategy has the potential to significantly mitigate severe urban haze by decreasing PM2.5 peak concentrations by more than 60% from above 300 µg m-3 to below 100 µg m-3, while requiring ~30% to 70% less emission controls as compared to complete emission reductions. The PAPCA strategy has the potential to tackle effectively severe urban haze pollution events with economic efficiency.

Predicted impact of thermal power generation emission control measures in the Beijing-Tianjin-Hebei region on air pollution over Beijing, China.

Widespread economic growth in China has led to increasing episodes of severe air pollution, especially in major urban areas. Thermal power plants represent a particularly important class of emissions. Here we present an evaluation of the predicted effectiveness of a series of recently proposed thermal power plant emission controls in the Beijing-Tianjin-Hebei (BTH) region on air quality over Beijing using the Community Multiscale Air Quality(CMAQ) atmospheric chemical transport model to predict CO, SO2, NO2, PM2.5, and PM10 levels. A baseline simulation of the hypothetical removal of all thermal power plants in the BTH region is predicted to lead to 38%, 23%, 23%, 24%, and 24% reductions in current annual mean levels of CO, SO2, NO2, PM2.5, and PM10 in Beijing, respectively. Similar percentage reductions are predicted in the major cities in the BTH region. Simulations of the air quality impact of six proposed thermal power plant emission reduction strategies over the BTH region provide an estimate of the potential improvement in air quality in the Beijing metropolitan area, as a function of the time of year.

Does temperature nudging overwhelm aerosol radiative effects in regional integrated climate models?

Nudging (data assimilation) is used in many regional integrated meteorology-air quality models to reduce biases in simulated climatology. However, in such modeling systems, temperature changes due to nudging could compete with temperature changes induced by radiatively active and hygroscopic short-lived tracers leading to two interesting dilemmas: when nudging is continuously applied, what are the relative sizes of these two radiative forces at regional and local scales? How do these two forces present in the free atmosphere differ from those present at the surface? This work studies these two issues by converting temperature changes due to nudging into pseudo radiative effects (PRE) at the surface (PRE_sfc), in troposphere (PRE_atm), and at the top of atmosphere (PRE_toa), and comparing PRE with the reported aerosol radiative effects (ARE). Results show that the domain-averaged PRE_sfc is smaller than ARE_sfc estimated in previous studies and this work, but could be significantly larger than ARE_sfc at local scales. PRE_atm is also much smaller than ARE_atm. These results indicate that appropriate nudging methodology could be applied to the integrated models to study aerosol radiative effects at continental/regional scales, but it should be treated with caution for local scale applications.

Characteristics and origins of air pollutants in Wuhan, China, based on observations and hybrid receptor models.

To identify the characteristics of air pollutants and factors attributing to the formation of haze in Wuhan, this study analyzed the hourly observations of air pollutants (PM2.5, PM10, NO2, SO2, O3, and CO) from March 1, 2013, to February 28, 2014, and used hybrid receptor models for a case study. The results showed that the annual average concentrations for PM2.5, PM10, NO2, SO2, O3, and CO during the whole period were 89.6 µg m-3, 134.9 µg m-3, 54.9 µg m-3, 32.4 µg m-3, 62.3 µg m-3, and 1.1 mg m-3, respectively. The monthly variations revealed that the peak values of PM2.5, PM10, NO2, SO2, and CO occurred in December because of increased local emissions and severe weather conditions, while the lowest values occurred in July mainly due to larger precipitation. The maximum O3 concentrations occurred in warm seasons from May to August, which may be partly due to the high temperature and solar radiation. Diurnal analysis showed that hourly PM2.5, PM10, NO2, and CO concentrations had two ascending stages accompanying by the two traffic peaks. However, the O3 concentration variations were different with the highest concentration in the afternoon. A case study utilizing hybrid receptor models showed the significant impact of regional transport on the haze formation in Wuhan and revealed that the mainly potential polluted sources were located in the north and south of Wuhan, such as Baoding and Handan in Hebei province, and Changsha in Hunan province. IMPLICATIONS: Wuhan city requires a 5% reduction of the annual mean of PM2.5 concentration by the end of 2017. In order to accomplish this goal, Wuhan has adopted some measures to improve its air quality. This work has determined the main pollution sources that affect the formation of haze in Wuhan by transport. We showed that apart from the local emissions, north and south of Wuhan were the potential sources contributing to the high PM2.5 concentrations in Wuhan, such as Baoding and Handan in Hebei province, Zhumadian and Jiaozuo in Henan province, and Changsha and Zhuzhou in Hunan province.

High reduction of ozone and particulate matter during the 2016 G-20 summit in Hangzhou by forced emission controls of industry and traffic.

Many regions in China experience air pollution episodes because of the rapid urbanization and industrialization over the past decades. Here we analyzed the effect of emission controls implemented during the G-20 2016 Hangzhou summit on air quality. Emission controls included a forced closure of highly polluting industries, and limiting traffic and construction emissions in the cities and surroundings. Particles with aerodynamic diameter lower than 2.5 µm (PM2.5) and ozone (O3) were measured. We also simulated air quality using a forecast system consisting of the two-way coupled Weather Research and Forecast and Community Multi-scale Air Quality (WRF-CMAQ) model. Results show PM2.5 and ozone levels in Hangzhou during the G-20 Summit were considerably lower than previous to the G-20 Summit. The predicted concentrations of ozone were reduced by 25.4%, whereas the predicted concentrations of PM2.5 were reduced by 56%.