[Spatial and Temporal Distribution Characteristics of Ozone Concentration and Population Health Benefit Assessment in the Yangtze River Delta Region from 2017 to 2020].

In recent years, the ozone (O3) concentration has showed a rising trend in China, becoming second only to PM2.5 as an important factor affecting air quality. To grasp the spatial-temporal variations characteristics of O3 and the associated health impacts during the implementation of the three-year plan on defending the blue sky in the Yangtze River Delta (YRD) region, data collected from 210 monitoring stations in the YRD from 2017 to 2020 were analyzed using the global Moran's index and Getis-Ord Gi* index methods, and the associated health benefits of reduced O3 exposure were evaluated using the health risk and environmental value assessment methods. The results showed that during the study period, the interquartile range (IQR) of the annual average O3 concentration and that of the warm season both presented a declining trend. The average O3 concentrations in both warm and cold seasons showed a similar spatial distribution pattern, with the northern part exhibiting the higher concentrations and the southern part showing the lower concentrations. Furthermore, the O3 concentrations in the warm season were characterized by high O3 concentrations clustering in the northern and central part of the region. The proportion of the population exposure to annual average O3 concentration over 160 µg·m-3 decreased from 72.3% in 2017 to 34.8% in 2020 in the YRD. Although the population-weighted annual mean O3 concentration in the whole YRD region showed a downward trend, some cities in western Anhui province, northern Jiangsu province, and central Jiangsu province showed fluctuations and even an increasing trend. In terms of health benefits, there were 3782 cases (95% CI:2050-5511 cases) of avoided premature deaths associated with reduced O3 concentrations in the warm season in 2020 compared to 2017. The total health benefit was 26198 million yuan (95% CI:14201-38175 million yuan). Compared to the cost of the main O3 precursor emission reduction, the cost-benefits ratio was 1:1.67 to 3.23.

[High-resolution Emission Inventory of Reactive Volatile Organic Compounds from Anthropogenic Sources in the Yangtze River Delta Region].

A high-resolution emission inventory of anthropogenic active volatile organic compounds (VOCs) for the Yangtze River Delta (YRD) region was developed based on the local measurement of 41 cities in the region and the specific 4.4 database of EPA. The emission characteristics and composition of VOCs were analyzed. The ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAP) of VOCs were calculated. The results showed that the total emission of anthropogenic VOCs in the YRD in 2017 was 4.9×106 t. The emission contributions of process sources, industrial solvent sources, mobile sources, domestic sources, storage and transportation sources, agricultural sources, and waste treatment sources were 34.3%, 27.1%, 19.5%, 9.7%, 6.1%, 2.5%, and 0.4%, respectively. Aromatic hydrocarbons and alkanes were the main components of VOCs, accounting for 25% of the total VOCs emissions in the region. The contribution rates of OFP from process sources, industrial solvent sources, mobile sources, and domestic sources were 38.3%, 21.5%, 16.4%, and 13.2%, respectively, and the contribution rates of SOAP were 26.2%, 34.1%, 18.1%, and 17.9%, respectively, which was basically consistent with the main contribution sources of VOCs emissions. The emission characteristics of the key industries in each city were obviously different. The key urban agglomeration of VOCs emission was mainly petrochemical industries and equipment manufacturing, whereas the northern part of the region was mainly wood furniture and other coating industries. The results showed that propylene, m-xylene, p-xylene, and ethylene were the main contribution sources of ozone, whereas toluene, 1,2,4-trimethylene, m-xylene, and p-xylene were the main contribution sources of secondary organic aerosols. In the next stage, the fine management of VOCs can be transformed into the main industries based on chemical reaction activity, which can give priority to the governance of key industries such as the chemical industry, petrochemical, automobile manufacturing, textile, wood, and furniture and can formulate different governance paths according to urban characteristics.

[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.

[Time-Determination and Contribution Analysis of Transport, Retention, and Offshore Backflow to Long-Term Sand-Dust Coupling].

Continuous on-line observation of particulate matter and PM2.5 chemical composition was conducted from October 15th to November 7th 2019 in East China. During the observation period, a wide range of dust-related processes took place. According to supplementary urban air quality assessment affected by dust (hereafter referred to as supplementary provisions), the observations were divided into four stages including pre-dust event, dust Ⅰ, dust Ⅱ, and post-dust event. The dust Ⅰ stage represented the processes of transportation and retention, while the dust Ⅱ stage represented processes of backflow from the sea and scavenging. The start time of the studied dust event was October 29th 08:00-09:00 based on the supplementary provisions, dust tracers, and air quality models; however, disagreements existed between these data sources with respect to the finishing time. The supplementary provisions could not effectively distinguish backflow dust from sea, and results from different dust tracers were variable. The WRF-CMAQ model simulated dust variation trends well but overestimated short-term suspended dust and backflow dust. PM10, PM2.5, and trace element concentrations were much higher during dust events than during non-dust periods, with highest daily concentrations of (234.8±125.5), (76.8±22.5), and (17.54±10.5) µg·m-3, respectively, which occurred on October 29th. During the dust event, concentration of crustal elements were remarkably high in PM2.5. At the same time, secondary ions (SO42-, NO3-, and NH4+) contributed less to PM2.5 mass concentrations. Four major crustal elements (Al, Si, Ca, and Fe) accounted for 23.5% and 13.7% of the mass concentration of PM2.5 and secondary ions accounted for 24.3% and 41.9% during dust Ⅰ and dust Ⅱ stages, respectively. Based on PMF source apportionment, Ca abundance, PM2.5/PM10 in dust sources, and the reconstruction of crustal material, dust particulates accounted for 43.4%-50.0% of PM2.5 and backflow dust accounted for 19.2%-24.7% of PM2.5.

[Characterization of Volatile Organic Compounds (VOCs) Using Mobile Monitoring Around the Industrial Parks in the Yangzte River Delta Region of China].

Volatile organic compounds (VOCs) play important roles in the formation of ozone and fine particles in the troposphere. Industrial parks emit significant amounts of VOCs in China, while few studies have characterized them. In the present study, a mobile platform was employed to measure the levels and composition VOCs around industrial parks in the Yangzte River Delta region. The average concentration of VOCs ranged from 39 µg·m-3 (5% percentile) to 533 µg·m-3 (95% percentile) with an average of 183 µg·m-3, which was three times that of ambient concentrations. Maximum VOC concentrations ranged from 307 µg·m-3 (5% percentile) to 12006 µg·m-3 (95% percentile) with an average of 2812 µg·m-3. The frequency of abnormal peak values was as high as 64% across all the industrial parks, of which toluene (32%), xylene (18%), benzene (9%), and>C9 aromatics (19%) were the most common species. Differences in VOC characteristics were observed among the different types of industrial parks. Specifically, highest concentrations of VOCs were observed in textile industrial parks followed by chemical, painting, and petrochemical industrial parks, and VOC concentrations in electronics industrial parks were the lowest. Importantly, species measured using the mobile platform only contributed~50% of VOCs present in ambient samples, indicating that the concentrations of VOCs in the industrial parks were underestimated overall. These results can inform measures to control VOC pollution in industrial parks in China.

[Particle Size Distribution of PM Emission from In-use Gasoline and Diesel Vehicles].

Particle size distribution and emission factors from 9 State 3-5 light-duty gasoline vehicles (LDGVs) and 15 State 3-5 heavy-duty diesel vehicles (HDDVs) were tested in this study using a constant volume sampling (CVS) system on a dynamometer. The influences of driving cycles and emission control level on the PM emission factors and particle size distribution were analyzed. The results show that the PM emission factors of the tested LDGVs and HDDVs were (4.1±4.0)×1014 and (5.7±4.3)×1015 kg-1, respectively; the HDDV PM emission factor was (14±7) times less than that of LDGVs. Regarding LDGVs, the PM emission factor under the extra high speed condition was much more than that of the other speed conditions at (5.1±5.0)×1013 km-1, 11.7, 14.1, and 7.3 times more than that under the low, medium, and high speed conditions, respectively. Regarding HDDVs, the emission factor under the high speed condition was 2.5 and 1.4 times that under the low and medium speed conditions, respectively, and was mostly of nuclei-mode particles. At the emission control level of State 3-5, the PM emission factors of LDGVs were (2.7±1.7)×1013, (2.6±1.3)×1013, and (1.6±1.2)×1013 km-1, respectively, and those of HDDVs were (2.2±1.2)×1015, 2.0×1015, and (7.1±2.1)×1014 km-1, respectively. With improvement in emission control level, the particle number emission control of LDGVs and HDDVs generally showed a good downward trend. However, the emission of PM above 110 nm from LDGVs did not improve with the emission control level. Although the quantity emission factor of HDDVs with particle size above 110 nm is relatively low, its harm to the environment cannot be ignored, which should justify necessary attention.

[Health Benefit Analyses of the Clean Air Action Plan Implementation in Shanghai].

To understand the public health benefits of the Clean Air Action Plan implemented in Shanghai from 2013-2017, the changes of the PM2.5 exposure levels and related health and economic benefits were quantitatively evaluated by using air quality numerical modeling, health risk assessment, and environmental valuation methods. The results show that the proportion of the population exposed to a mean annual PM2.5 concentration lower than or equal to 35 µg·m-3 has increased from 1.62% in the base year to 34.06% in the control year. The death risk attributable to ambient PM2.5 exposure decreased from 15.2% in the base year to 11.9% in the control year. The total health benefits are approximately 11.841 billion RMB(95% CI:5.024-17.819 billion RMB), accounting for 0.55%(95% CI:0.23%-0.82%)of Shanghai's GDP in 2013. The implementation of the action plan has a positive effect on the protection of the health of the population. Health benefits in areas with dense populations and high PM2.5 declines are more pronounced within the outer ring line of Shanghai City.

[Emission Inventory and Prediction of Non-road Machineries in the Yangtze River Delta Region, China].

An air pollutant emission inventory of non-road machineries for the Yangtze River Delta (YRD) region was developed, based on local surveys and relative indicator predictions for cities in the region. Population, fuel consumption, and air pollutant emissions of non-road machineries were predicted for the period 2005 to 2025. The population of non-road machineries in the YRD region in 2014 was 8.23×106 units, diesel consumption was about 9.95×106 t, and SO2, NOx, CO, VOCs, PM10, and PM2.5 emissions were 5.5×103, 4.9×105, 7.6×105, 1.1×105, 2.9×104, and 2.7×104 t, respectively. Agricultural machineries accounted for 93% of the total population, with their CO and VOC emissions contributing 88% and 77% of respective totals. Construction machineries contributed 49% and 35% of NOx and PM2.5 emissions. Air pollutant emissions from non-road machineries were mainly concentrated in the middle and northern cities of the YRD region. During the period 2005-2014, the growth rates of population, fuel consumption, and air pollutant emissions of non-road machineries in the YRD region were relatively high. It is estimated that growth will be slowing down in 2020 and 2025. Diesel consumption will increase by 2% and 8% in 2020 and 2025, respectively, compared with 2014 levels. By 2020, SO2, NOx, CO, VOCs, PM10, and PM2.5 emissions will decrease by 97%, 10%, 3%, 10%, 11%, and 11%, respectively; by 2025, these decreases will reach 97%, 16%, 3%, 15%, 21%, and 21%, respectively. It is expected that air pollutant emissions from non-road machineries will continue to decline in future. However, the decreasing trend of NOx, VOCs, and PM2.5 emissions from motor vehicles reached 22%, 50%, and 48%, much greater than that of non-road machinery. The emission contributions of non-road machinery will become increasingly significant in future. It is necessary to accelerate the scrappage of old machinery and to further promote emission standards for new machinery to reduce emissions from non-road machineries.

[Emissions, Chemical Composition, and Spatial and Temporal Allocation of the BVOCs in the Yangtze River Delta Region in 2014].

Based on the land surface vegetation data interpreted via remote sensing and the meteorological conditions predicted via the WRF model, the MEGAN model was applied to calculate the regional BVOC emissions in the Yangtze River Delta (YRD) in 2014. The chemical components and the temporal and spatial allocations were further analyzed. Results show that the annual BVOC emissions in the YRD were 1886 kt, in which isoprene emissions were 704.2 kt (accounting for 37.3%), monoterpenes 303 kt (16.1%), and other VOCs 878.8 kt (46.6%). Seasonal variation of the BVOC emissions was very significant. The BVOC emissions had a strong seasonal pattern, with maximum emissions in summer, accounting for 60.9% (1088 kt) of the total, whereas the minimum emissions occurred in winter, accounting for 3.2% (57 kt). Spatially, the southern YRD produced more BVOC emissions than the northern part did. In Zhejiang, Anhui, Jiangsu, and Shanghai, the BVOC emissions were 842 kt (44.6%), 760 kt (40.3%), 272 kt (14.4%), and 12 kt (0.7%), respectively. This is mainly related to the distribution of vegetation types.

[Ozone source apportionment at urban area during a typical photochemical pollution episode in the summer of 2013 in the Yangtze River Delta].

With the fast development of urbanization, industrialization and mobilization, the air pollutant emissions with photochemical reactivity become more obvious, causing a severe photochemical pollution with the characteristics of high ozone concentration. However, the ozone source identification is very complicated due to the high non linearity between ozone and its precursors. Thus, ways to reduce ozone is still not clear. A high ozone pollution episode occurred during July, 2013, which lasted for a long period, with large influence area and high intensity. In this paper, we selected this episode to do a case study with the application of ozone source apportionment technology(OSAT) coupled within the CAMx air quality model. In this study, 4 source regions(including Shanghai, north Zhejiang, South Jiangsu and long range transport), 7 source categories (including power plants, industrial process, industrial boilers and kilns, residential, mobile source, volatile source and biogenic emissions) are analyzed to study their contributions to surface O3 in Shanghai, Suzhou and Zhejiang. Results indicate that long range transport contribution to the surface ozone in the YRD is around 20 x 10(-9) - 40 x 10(-9) (volume fraction). The O3 concentrations can increased to 40 x 10(-9) - 100 x 10(-9) (volume fraction) due to precursors emissions in Shanghai, Jiangsu and Zhejiang. As for the regional contribution to 8 hour ozone, long range transport constitutes 42.79% +/- 10.17%, 48.57% +/- 9.97% and 60.13% +/- 7.11% of the surface ozone in Shanghai, Suzhou and Hangzhou, respectively. Regarding the high O3 in Shanghai, local contribution is 28.94% +/- 8.49%, north Zhejiang constitutes 19.83% +/- 10.55%. As for surface O3 in Suzhou, the contribution from south Jiangsu is 26.41% +/- 6.80%. Regarding the surface O3 in Hangzhou, the major regional contributor is north Zhejiang (29.56% +/- 8.33%). Contributions from the long range transport to the daily maximum O3 concentrations are slightly lower than those to the 8-hourly O3, with the contribution of 35.35%-58.04%, while local contributions increase. As for the contributions from source sectors, it is found that the major source contributors include industrial boilers and kilns (18.4%-21.11%), industrial process (19.85%-28.46%), mobile source (21.30%-23.51%), biogenic (13.01%-17.07%) and power plants (7.08%-9.75%). Thus, industrial combustion, industrial processes, and mobile source are major anthropogenic sources of high ozone pollution in summer in the YRD region.

[Source Contribution Analysis of the Fine Particles in Shanghai During a Heavy Haze Episode in December, 2013 Based on the Particulate Matter Source Apportionment Technology].

The haze pollution caused by high PM2.5 concentrations has become one of the major environmental issues restricting urban and regional sustainable development in China in recent years. Therefore, the diagnosis of the pollution sources of PM2.5 and its major components in a scientific and efficient way is of great significance both scientifically and theoretically. A rare heavy haze pollution event occurred in Shanghai and the surrounding Yangtze River Delta in early December, 2013, that the hourly PM2.5 concentration reached 640 µg x m(-3). In this study, we analyzed the three typical episodes that occurred in Shanghai during this period. The particulate matter source apportionment technology (PSAT) was applied to study the source contributions to PM2.5 and its major components. Results showed that NO3-(2.5) were mostly contributed by industrial boilers and kilns, transportation and power plants. Comparatively, most of the SO4(2-) 2.5 came from industry and transport sectors. During the three episodes including haze, foggy haze and transport, local emissions contributed 35.3%, 44.8%, 22.7%, while super-regional transport accounted for 42.0%, 41.1% and 59.8% to PM2.5, respectively. In the YRD modeling domain, fugitive dust, industrial processing, volatile source, industrial boilers and kilns and transport were the major contributors to high concentrations of PM2.5, with the average contributions of 25.1%, 14.9%, 15.8%, 13.7% and 15.9%, respectively. Results showed that the very heavy haze pollution is usually not caused by a single city, the regional joint pollution control is of great importance to relieve the pollution level.