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To clarify the pollution characteristics and sources of atmospheric VOCs in Zhengzhou City in the summer, multi-site offline sampling and laboratory analyses of atmospheric VOCs in Zhengzhou were carried out in August 2022. The observed and initial VOC volume fraction levels, OFP, SOAFP, and sources were compared. During the study period, the average values of three-site observation and initial φ （VOCs） during the study period were （31.83 ±13.51）×10 -9 and （35.92 ±15.30）×10 -9 ,respectively. Olefins （52.5 %） and aromatic hydrocarbons （29.7 %） were the components with a higher photochemical loss rate, and the spatial variations of the observed TVOCs concentration at each site were： Zhengzhou University （ZZU） > Gangli Reservoir （GLR） > Jingkaiqu （JKQ）, and the concentrations of alkanes and OVOCs at each site were higher. Olefins and aromatic hydrocarbons were the components that contributed greatly to the formation of O 3 and SOA. Motor vehicle sources, solvent-use sources, and industrial sources were the main contributing sources of atmospheric VOCs in Zhengzhou. Compared with the source analysis results based on the initial concentration, the contribution rates of motor vehicle sources, industrial sources, and solvent use sources were relatively high, and the contribution rates of combustion sources, plant sources, and oil and gas volatilization sources were relatively low.

Organic acids in atmospheric particulate matter are widely involved in various physical and chemical reactions in the atmosphere and contribute greatly to the formation of secondary organic aerosols and haze pollutions. Therefore， the concentration distribution characteristics， sources， and secondary formation of organic acids in particulate matter are of great significance for further investigation of organic aerosols and their secondary transformation. Fine particulate matter （PM 2.5 ） samples were collected in Zhengzhou， and three types of organic acids， including dicarboxylic acids， fatty acids， and resin acids， were analyzed to explore their species distribution， seasonal variations， source contribution， and secondary generation. Malonic acid （di-C 3 ） and succinate acid （di-C 4 ） were the most abundant in the identified dicarboxylic acids， which showed obvious seasonal variations in the order of summer > autumn > winter > spring. Fatty acids had the highest concentration in winter and the lowest concentration in spring， showing obvious bimodal advantages， with the most abundant compounds being palmitic acid and stearic acid （C 18 ）. Principal component analysis and multiple linear regression （MLR） were used to analyze the source of organic acids in PM 2.5 in Zhengzhou； the results showed that 35% of the organic acids came from combustion and traffic sources， 24% from cooking sources， 23% from secondary formation， and 17% from natural sources. The ratios of the selected marker species （i.e.， di-C 3 / di-C 4 ， F/M， and C 18：1 / C 18 ） were used as tracers for the secondary formation of the organic aerosol and its aging process. The results showed that the photochemical reaction was intense in summer， and the proportion of organic aerosol aging or secondary production was high， whereas the photochemical reaction was weak in winter， and the aging degree of organic aerosol was low. Correlation analysis and MLR were used in combination to quantify the relative contribution of gas-phase oxidation and liquid-phase oxidation to dicarboxylic acid formation， and the results showed that gas-phase oxidation played a dominant role in the sampling period （accounting for 58%）， especially in summer （61%）.

In order to study the pollution characteristics of volatile organic compounds （VOCs）， continuous monitoring of VOCs in two pollution processes was conducted in June and December 2021 in Zhengzhou. Combined with meteorological conditions， the pollution characteristics， source contributions， and reactivity of VOCs in winter and summer were compared and analyzed. The results showed that the volume fraction of atmospheric VOCs in two episodes were （27.92±12.68）×10 -9 and （24.30±5.93）×10 -9 ， respectively. The volume fraction of atmospheric VOCs in the haze pollution process in winter was larger than that in the ozone pollution process in summer. The analysis results of winter sources were as follows： industrial source （27.0%）， motor vehicle source （22.5%）， combustion source （20.1%）， solvent use source （16.3%）， and oil and gas volatilization source （14.1%）. The analysis results of summer sources were as follows： motor vehicle source （24.8%）， industrial source （24.1%）， solvent source （17.4%）， oil and gas volatilization source （14.2%）， combustion source （11.2%）， and plant source （8.4%）. The results of the smog production model showed that the proportion of days in the synergistic control zone of VOCs during the two pollution processes in summer （66.7%） was smaller than that in winter （100.0%）. The secondary reaction activity results showed that the average ·OH loss rate （ L ·OH ） values in winter and summer were 4.12 s -1 and 4.75 s -1 ， respectively. The average ozone formation potential （OFP） values in summer were 108.36 μg·m -3 . The olefins were dominant in the top ten species due to L ·OH and OFP contributions in summer. The total SOAFP values in winter in Zhengzhou were 54.38 μg·m -3 . Among the top ten species contributing to SOAFP in winter， nine were aromatic hydrocarbons.

Nitrate (NO 3 - ), sulfate (SO 4 2- ), and ammonium (NH 4 + ) are important components of PM 2.5 , and studying their characteristics and influencing factors is essential for the continuous improvement of air quality. A series of online instruments were used to analyze the chemical components of PM 2.5 in Zhengzhou in the summer of 2020. The results showed that the average ρ (PM 2.5 ) was (28 ±13) μg·m -3 , showing a daily variation characteristic of high at night and low during the day. The main concentrations of NO 3 - , SO 4 2- , and NH 4 + were (7.8 ±6.7), (7.2 ±3.7), and (5.5 ±3.1) μg·m -3 , accounting for 22%, 21%, and 16% in PM 2.5 , respectively. The proportions of NO 3 - (27%) and SO 4 2- (23%) in PM 2.5 , respectively, increased with the increase in PM 2.5 and O 3 concentration. In addition, the proportions of NO 3 - and NH 4 + increased under low wind speed, high humidity, low temperature, and rainfall conditions. Moreover, the proportion of NO 3 - showed a daily variation characteristic of high at night and low during the day, whereas the opposite was true for SO 4 2- . The gas-particle partitioning process of NH 4 NO 3 was the main factor affecting the concentrations of NO 3 - and NH 4 + in PM 2.5 . Low temperature, high humidity, and high aerosol water content concentrations favored the partitioning of HNO 3 and NH 3 to the particulate phase. High pH also favored the partitioning of gas-phase HNO 3 to NO 3 - ; however, it was not conducive to the partition of NH 3 to NH 4 + . These trends partially explained the increase in the concentration and proportion of NO 3 - in PM 2.5 under different scenarios.

To investigate the change characteristics of secondary inorganic ions in PM 2.5 at different pollution stages before and after COVID-19, the online monitoring of winter meteorological and atmospheric pollutant concentrations in Zhengzhou from December 15, 2019 to February 15, 2020 was conducted using a high-resolution (1 h) online instrument. This study analyzed the causes of the haze process of COVID-19, the diurnal variation characteristics of air pollutants, and the distribution characteristics of air pollutants at different stages of haze.The results showed that Zhengzhou was mainly controlled by the high-pressure ridge during the haze process, and the weather situation was stable, which was conducive to the accumulation of air pollutants. SNA was the main component of water-soluble ions, accounting for more than 90%. Home isolation measures during COVID-19 had different impacts on the distribution characteristics of air pollutants in different haze stages. After COVID-19, the concentration of PM 2.5 in the clean, occurrence, and dissipation stages increased compared with that before COVID-19 but significantly decreased in the development stage. The home isolation policy significantly reduced the high value of PM 2.5 . The concentrations of NO 2 , SO 2 , NH 3 , and CO were the highest in the haze development stage, showing a trend of first increasing and then decreasing. The concentration of O 3 was lowest in the pre-COVID-19 development stage but highest in the post-COVID-19 development stage. The linear correlation between[NH 4 + ]/[SO 4 2- ] and[NO 3 - ]/[SO 4 2- ] at different time periods before and after COVID-19 was strong, indicating that the home isolation policy of COVID-19 did not change the generation mode of NO 3 - , and the corresponding reaction was always the main generation mode of NO 3 - . The correlation between[excess-NH 4 + ] and[NO 3 - ] was high in different periods before COVID-19, and NO 3 - generation was related to the increase in NH 3 or NH 4 + in the process of PM 2.5 pollution in Zhengzhou.

In recent years, complex air pollution with the characteristic pollutant of PM 2.5 has remained serious in China. Long term exposure to PM 2.5 might harm residential health and can increase premature death from specific diseases. The annual average concentration of PM 2.5 in Zhengzhou was much higher than the national secondary standard, which has an extremely negative impact on the health of residents. Based on the high spatial resolution grids of population density established through web-crawling and outdoor monitoring concentrations and urban residential emissions used to evaluate PM 2.5 exposure concentration, the exposure concentration of PM 2.5 for urban residents of Zhengzhou was assessed, considering both indoor and outdoor exposures. Relevant health risks were quantified with the integrated exposure-response model. Finally, the contributions of various reducing measures and different standards of air quality to the decreases in PM 2.5 exposure concentration were analyzed. The results showed that in 2017 and 2019, the time weighted exposure concentrations of PM 2.5 for Zhengzhou's urban residents were 74.06 μg·m -3 and 60.64 μg·m -3 , respectively, which was decreased by 18.12%. In addition, the mass fractions of the indoor exposure concentrations in the time weighted exposure concentrations were 83.58% and 83.01%, and its contribution to the drop of the time weighted exposure concentrations was 84.06%. In 2017 and 2019, the numbers of premature deaths attributed to PM 2.5 exposures for urban residents of Zhengzhou over the age of 25 were 13285 and 10323, respectively, showing a 22.30% decrease. By using these comprehensive measures, PM 2.5 exposure concentration for Zhengzhou's urban residents could be reduced by 86.23% at most, and 8902 premature deaths could be avoided.

From July 2020 to June 2021, monthly offline sampling of atmospheric VOCs was carried out and analyzed at three urban sites and one suburban site in Zhengzhou. Then, the volume fraction levels, composition characteristics, reactivity, and source apportionment of atmospheric VOCs were discussed. The results showed that the volume fraction of atmospheric VOCs in Zhengzhou was (37.50±14.30)×10 -9 during the sampling period, and the proportion of components was represented by alkanes (33%)>OVOCs (24%)>halogenated hydrocarbons (23%)>aromatic hydrocarbons (8%)>alkenes (7%)>alkynes (4%)>sulfides (1%). The seasonal variation characteristics were winter>autumn>summer>spring, and the monthly average value of VOCs had the highest value in January and the lowest value in May; the spatial variation characteristics were Zhengzhou University (ZD)>Jiancezhan (JCZ)>Jingkaiqu (JKQ)>Gangli Reservoir (GLR). The average·OH loss rate ( L ·OH ) was 4.24 s -1 , and the average ozone formation potential (OFP) was 172.27 μg·m -3 ; the top ten species of L ·OH and OFP at each site and in each season were dominated by alkenes, OVOCs, and aromatic hydrocarbons. The results of positive matrix factorization (PMF) showed that the main sources of VOCs were vehicle emissions (28%), solvent utilization (24%), industrial emissions (24%), and oil and gas volatilization (19%) and plant emissions (5%).

In recent years, the Beijing-Tianjin-Hebei region and its surrounding areas have experienced multiple haze pollution processes. Owing to the limitation of observational instruments, there has not been a comparative study of haze pollution between urban and rural areas in northern Henan province. A series of high-time-resolution instruments were used during a regional heavy pollution process (January 12-25, 2018) at two urban sites and three rural sites. The results showed that SO 4 2- , NO - 3 , and NH + 4 (SNA) were the components with the highest proportion in PM 2.5 at the five sites during the haze event with a range of 53%-63%, of which nitrate was the most important, accounting for 24%-32%, followed by sulfate, ranging from 13%-17%. Compared with urban sites, rural sites were more affected by organic matter, especially at night. With the aggravation of pollution, the proportion of SNA increased, reaching 67% during periods of heavy pollution. When the area was affected by the air mass transported from the south, the proportion of NO - 3 in PM 2.5 increased, and when the area was affected by the air transport in the north, the proportions of SO 4 2- and organic matter increased. Ammonium nitrate was the most important component that led to the decrease in atmospheric visibility during the haze process. Moreover, the contributions of ammonium nitrate and ammonium sulfate at the urban sites were higher than those at the rural sites. To summarize, there were significant differences in PM 2.5 components between the urban and rural sites. Urban areas need to continue to strengthen the reduction in gaseous precursors, and rural areas need to pay attention to the sources of carbonaceous aerosol.

Air pollution in Henan province is serious and is significantly impacted by pollution transmission and interactions with surrounding areas. The emission sources in 18 cities in Henan province were labeled and applied to the WRF-CMAQ traceability model for simulation in January, April, July, and October of 2017. The pollutant distribution results showed that due to the combined influence of emissions and meteorology, the concentrations of PM 2.5 , NO 2 , and SO 2 in Henan province were the highest in winter and the lowest in summer. The seasonal variation in O 3 -8h concentration was the highest in summer, followed by spring, and the lowest in winter. There was a large difference in pollutant concentrations between different seasons. The average concentrations of PM 2.5 , NO 2 , and SO 2 in winter in Henan province were 4.17, 4.12, and 6.24 times those in summer, respectively, whereas the concentration of O 3 -8h in summer was 2.24 times that in winter. Since PM 2.5 , NO 2 , and SO 2 are closely related to primary emissions and have a certain homology, the distributions of high values of these three pollutants were higher in the north and lower in the south, and the seasonal trends were more consistent. The seasonal distribution of O 3 -8h varied widely, with high O 3 -8h values mainly distributed in the northeastern region of Henan province in summer, when meteorological conditions contributed to O 3 production; in winter, spring, and autumn, high O 3 -8h values were mainly distributed in the southern part of Henan province due to the suppression of meteorological conditions and NO x consumption. The results of the study on the transport of pollutants showed that extra-provincial transport and natural sources contributed the most to the concentrations of PM 2.5 , O 3 -8h, NO 2 , and SO 2 in winter, with 36.20%-72.32%, 77.96%-96.08%, 49.45%-78.80%, and 59.05%-88.85%, respectively. When considering only local emissions and intra-provincial transmission, the contributions of emissions to local concentrations of the four pollutants in summer were the highest in all cities of Henan province. The contributions of intra-provincial transmission to PM 2.5 and O 3 -8h in spring were the largest, with 25.63%-74.69% and 30.21%-80.01%, respectively, and the contributions of intra-provincial transmission to NO 2 and SO 2 in winter were larger, with 26.02%-76.96% and 20.30%-82.34%. The transmission paths of PM 2.5 , NO 2 , and SO 2 were more similar in Henan province, with more transmission from north to south in winter, from west to east and southwest to northeast in spring, from southwest to northeast in summer, and from north to south in autumn; however, the transmission of PM 2.5 was more complicated. The O 3 -8h transport path was more different from the others, especially in autumn when pollutants were mostly transported from north to south, but the O 3 -8h transport path from southwest to northeast was obvious.

Based on air quality station data and satellite remote sensing data, the interannual variation characteristics and seasonal variation trends of near-surface ozone (O 3 ) in Henan province were studied, and the variation in O 3 sensitivity was analyzed. The results showed that the O 3 concentration near the surface of Henan province increased first and then decreased from 2015 to 2020. The highest O 3 concentration was found in 2018, and the annual mean of the maximum daily 8 h moving mean (MDA8) of O 3 was 110.70 μg·m -3 . The difference in MDA8 values among different stations gradually decreased. From 2015 to 2020, the average monthly MDA8 in Henan province showed an upward trend, with a growth rate of 2.46 μg·(m 3 ·a) -1 . According to the MK trend test, except for in Luohe, Nanyang, and Pingdingshan, the rising trend in other cities was significant ( P <0.05). The concentration of MDA8 in the four seasons also showed an increasing trend during the 6 years as follows:autumn (19.31%)>winter (17.09%)>spring (16.82%)>summer (7.24%). From 2015 to 2019, the high value of tropospheric NO 2 was concentrated in the northwest of Henan province, and the concentration showed a decreasing trend with a decreasing rate of 0.34×10 15 molecules·(cm 2 ·a) -1 , whereas the tropospheric HCHO showed a slow rising trend with an annual growth rate of 0.19×10 15 molecules·(cm 2 ·a) -1 , with a higher concentration in the northern urban area. The O 3 sensitivity control area from 2015 to 2019 showed that most of the eastern part of Henan province belonged to the VOCs limited category.

The COVID-19 lockdown was a typical occurrence of extreme emission reduction, which presented an opportunity to study the influence of control measures on particulate matter. Observations were conducted from January 16 to 31, 2020 using online observation instruments to investigate the characteristics of PM 2.5 concentration, particle size distribution, chemical composition, source, and transport before (January 16-23, 2020) and during (January 24-31, 2020) the COVID-19 lockdown in Zhengzhou. The results showed that the atmospheric PM 2.5 concentration decreased by 4.8% during the control period compared with that before the control in Zhengzhou. The particle size distribution characteristics indicated that there was a significant decrease in the mass concentration and number concentration of particles in the size range of 0.06 to 1.6 μm during the control period. The chemical composition characteristics of PM 2.5 showed that secondary inorganic ions (sulfate, nitrate, and ammonium) were the dominant component of PM 2.5 , and the significant increase in PM 2.5 was mainly owing to the decrease in NO 3 - concentration during the control period. The main sources of PM 2.5 identified by the positive matrix factorization (PMF) model were secondary sources, combustion sources, vehicle sources, industrial sources, and dust sources. The emissions from vehicle sources, industrial sources, and dust sources decreased significantly during the control period. The results of analyses using the backward trajectory method and potential source contribution factor method indicated that the effects of transport from surrounding areas on PM 2.5 concentration decreased during the control period. In summary, vehicle and industrial sources should be continuously controlled, and regional combined prevention and control should be strengthened in the future in Zhengzhou.

To explore the emission characteristics of volatile organic compounds (VOCs) from vehicular exhaust sources and evaporative sources with ethanol gasoline (E10) as the main fuel, VOCs sampling campaigns were carried out in the north third ring tunnel of Zhengzhou city for two consecutive weeks in December 2019. In addition, the characteristics of traffic flow and environmental information were also monitored in the tunnel. Firstly, 106 VOCs were quantified using gas chromatography/mass spectrometry (GC/MS), and then source apportionment of VOCs in the tunnel was carried out using a positive matrix factorization (PMF5.0)-chemical mass balance (CMB8.2) composite model. Finally, the ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) of vehicle exhaust sources and evaporative sources were analyzed using the maximum incremental reactivity (MIR) and fractional aerosol coefficient (FAC). The results showed that ρ (VOCs) in the tunnel was (2794.5±147.4) μg·m -3 during the experiment, among which halogenated hydrocarbons[(32.4±2.0)%] accounted for the highest proportion, followed by aromatic hydrocarbons[(27.5±0.6)%] and alkanes[(23.3±0.8)%]. Source apportionment of vehicular VOCs showed that exhaust emissions (62.5%)>evaporative emissions (37.5%), whereas the contribution of OFP was that exhaust emissions (71.9%)>evaporative emissions (28.1%), and the contribution of SOAFP was that exhaust emissions (75.8%)>evaporative emissions (24.2%). The dominant components of OFP in evaporative sources were m,p -diethylbenzene, isoprene, and trans -2-pentene, whereas m,p -diethylbenzene, m,p -xylene, and 1,2,3-trimethylbenzene were the dominant components of SOAFP. The major components of OFP in exhaust sources were m,p -xylene, 1,2,4-trimethylbenzene, and 1,3,5-trimethylbenzene, whereas m,p -xylene, m,p -diethylbenzene, and 1,3,5-trimethylbenzene were the dominant components of SOAFP. In regions where ethanol gasoline is used, special attention should be paid not only to the exhaust emissions control but also to strengthening the emissions reduction of VOCs from vehicle evaporative sources, especially the high active components such as aromatic hydrocarbons and alkenes.

Heavy metal elements in particulate matter can cause adverse effects on human health, and the smaller the particle size, the greater the harm. A total of 16 heavy metal elements (Al, Si, K, Ca, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Se, Ba, Pb, and Cd) in PM 1 were continuously determined by an online heavy metal observation instrument in Zhengzhou city from January 7 to 25, 2021. The results showed that ρ (K) concentration was the highest during the observation period (0.62 μg·m -3 ). According to pollutant concentration and meteorological characteristics, the observation period was divided into clean days, dust days, and haze days. The contribution of heavy metal pollution characteristics and health risk assessment in atmospheric PM 1 was different under different pollution processes. The US EPA health risk assessment method was used to assess the health risks of heavy metals, and the enrichment factor method and positive matrix factorization (PMF) were used to analyze the sources of heavy metals. The influence of the transmission was evaluated by using the concentration-weighted trajectory (CWT) method and the backward trajectory method. The results show that the enrichment factors of Zn, As, Se, Pb, and Cd were more than 100 under different pollution processes, which were greatly affected by human activities. During the sampling period, the main sources of heavy metals were industrial sources, coal/biomass sources, motor vehicle sources, and dust sources. The results of the health risk assessment were substituted into PMF analysis, and it was found that industrial sources were the main contributing sources of carcinogenic and non-carcinogenic health risks during cleaning days, dust days, and haze days, and the carcinogenic risk of heavy metal elements in PM 1 in this region for adults exceeded that for children. CWT and backward trajectory methods revealed that regional transmission was one of the main factors affecting local health risks.

To reduce the risks of COVID-19 on society and the health of the general public, necessary prevention and control measures were implemented throughout China in 2020. Consequently, air quality was greatly improved due to lower emissions. However, the improvement of air quality could also be closely related to meteorological conditions. During quarantine (January 27 to February, 2020), reductions were observed in the concentration of all air pollutants in Henan Province (PM 2.5 , PM 10 , SO 2 , CO, and NO 2 decreased by 36.89%, 34.18%, 19.43%, 29.85%, and 58.51%, respectively) relative to measurements taken from January 1 to 26, 2020. The only exception was for the concentration of O 3 , which increased by 69.64%. This study evaluates the importance of meteorological conditions in air pollution, through simulation with a long-and-short-term memory network (LSTM) and a machine learning algorithm. Results show that meteorological conditions play a crucial role in air pollutant formation. Given favorable meteorological factors, the concentrations of pollutants could be reduced by 15%-30%, while the reduction due to anthropogenic emission control ranges from 6%-40%. During the epidemic, meteorological conditions and human emissions accounted for 34.84% and 34.81% of the increase in O 3 concentration, respectively. The results show that primary pollutant concentrations are more sensitive to the intensity of anthropogenic emissions. However, secondary pollutants are more dependent on meteorological factors. Furthermore, a nonlinear relationship has been identified between O 3 concentration and to emission intensity. Further investigation into the causes of the rise in O 3 concentration is necessary to gain a greater understanding and better control of particulate matter and O 3 pollution.

Based on the collected urban motor vehicle activity ownership and traffic flow of highways, combined with the mileage and source profiles of VOCs, using the emission factor method, we established high-resolution emission inventories from 2016 to 2019 for urban and 2016-based highway motor vehicles, respectively, in Henan Province, China. The results showed that gasoline vehicles, particularly minibuses and ordinary motorcycles, were the main contributors of CO, VOCs, and NH 3 , whereas heavy-duty and light-duty diesel trucks emitted SO 2 , NO x , and PM. Vehicles with China 1, China 3, and China 4 emission standards contributed significantly to pollutant emissions in the fleet. The temporal variation in traffic flow was consistent with the changes in freight and passenger traffic, with higher coefficients of variation for highways from August to October and the lowest in November. The weekly and daily changes in urban trunk roads showed distinct weekend effects and clear double-peak features, respectively. High-value emission areas were concentrated in urban centers with dense transport networks and high traffic volumes and on roads radiating outward from urban areas. The Lianhuo Expressway and the Beijing-Hong Kong-Macau Expressway were high-emission roads. Light-duty gasoline vehicles made the largest contribution to the ozone formation potential (OFP) of VOCs from motor vehicles. Five species, such as ethylene and propylene, contributed significantly to VOC emissions and OFP. The average annual growth rate of vehicle ownership from 2016 to 2019 was 5.7%. Compared with 2016, VOC emissions increased by 2.8% in 2019, whereas emissions of other pollutants showed decreasing trends of different degrees, with decreases of 76.3%, 51.7%, 50.3%, 43.1%, 16.7%, and 5.9% for SO 2 , PM 2.5 , PM 10 , NH 3 , CO, and NO x , respectively. The emission reduction percentage of each pollutant in 2019 under the control policies relative to the baseline scenario ranged from 15.6% to 82.4%.

To explore the main sources of PM 2.5 and the characteristics of seasonal differences in Zhengzhou, PM 2.5 sampling was conducted in 2019 and the concentrations of inorganic water-soluble ions, carbon components, and various elements were analyzed. Results showed that the average mass concentration of PM 2.5 in 2019 was (67.0±37.2) μg ·m -3 with the highest concentration in winter and the lowest in summer. The main components of PM 2.5 were nitrate, ammonium, sulfate, organic matter, crustal matter, and elemental carbon. In spring and autumn, PM 2.5 was greatly affected by crustal matter and elemental carbon, and In summer, concentrations were mainly affected by sulfate. In winter, the concentrations of organic matter and nitrate increased significantly, produced by photochemical reactions in summer and aqueous-phase reactions under high humidity in winter. Carbonaceous aerosols were greatly influenced by automobile exhaust emission, coal combustion, and biomass combustion. Source apportionment showed that secondary sources were the greatest contributors in all four seasons, particularly in in winter (56.5%). Among the primary sources, the proportion of dust in spring (15.2%) and autumn (11.4%) was slightly higher, and the contribution of motor vehicle pollution was the largest (12.3%) in summer. In winter, PM 2.5 was greatly affected by coal combustion (13.2%). From 2014 to 2019, PM 2.5 in Zhengzhou increased annually under the influence of secondary sources. The contribution of industrial sources, biomass combustion sources, and coal combustion sources exhibited a downward trend over this period.

To study the spatiotemporal variations in fine particulate matter (PM 2.5 ) and the impact of air quality management in autumn and winter in Zhengzhou, five sites were selected to collect PM 2.5 samples from the autumn of 2017 to the winter of 2018, and the characteristics of the chemical components were analyzed. The positive matrix factorization (PMF) model was also applied to identify the sources of PM 2.5 , and the effect of air quality control was evaluated to provide support for air quality control in autumn and winter in the next stage. The PM 2.5 concentrations in the four seasons in Zhengzhou were ranked as winter > autumn > spring > summer. The PM 2.5 concentration at Zhengzhou University (ZZU) was the highest (8.7% higher than the average concentration), and the PM 2.5 concentrations at the other sites were slightly lower than the average concentration. The concentration of water-soluble ions (WSIs) was low in spring and summer and high in autumn and winter. The average proportions of SO 4 2- , NO 3 - , and NH 4 + in the nine WSIs were as high as 22.5%, 43.6%, and 23.4%, respectively. The proportion of Cl - in winter was higher than that in the other seasons owing to coal combustion (6.7% and 6.6% in 2017 and 2018, respectively). Owing to wind and sand, the proportions of Ca 2+ and Mg 2+ in spring were the highest (4.4% and 0.4%, respectively), and those at the Jiancezhan (JCZ) and ZZU sites were higher than those at the other sites. K + , as a marker of biomass burning, had a higher proportion in spring, autumn, and winter. The proportion of K + in the spring of 2018 was 1.9%, those in the autumn and winter of 2017 were 1.6% and 2.1%, respectively, and those in the autumn and winter of 2018 were 1.3% and 1.8%, respectively. JCZ, Hangkonggang (HKG), and Xinmi (XM) had higher proportions of NO 3 - , and the proportions of SO 4 2- were lower. Secondary organic carbon (SOC) pollution was serious in autumn and winter, and the concentration accounted for more than half of the organic carbon (OC). In 2018, the SOC/OC at the JCZ and ZZU sites decreased compared with that in 2017, but that at the other three sites increased significantly, thereby indicating that different air pollutant emissions in these regions had different performances in response to control policies. The chemical composition reconstruction results showed that the proportion of sulfate was highest in summer (25.0%), the contribution of nitrate was higher in autumn (23.1% and 25.1% for 2017 and 2018, respectively) and winter (20.6% and 23.0% for 2017 and 2018, respectively), the proportion of crustal material was higher in spring (18.2%), and the contribution of secondary organic aerosol (SOA) was the highest in winter (14.1% and 20.5% for 2017 and 2018, respectively). SOA had higher contributions at the JCZ and HKG sites (16.9% and 16.4%, respectively), and ZZU was affected more by primary organic aerosol (14.3%) and crustal materials (12.1%). The PMF results showed that secondary inorganic salts (37.5%), SOA (15.4%), traffic (14.9%), industry (4.8%), coal combustion (16.0%), fugitive dust (6.5%), and biomass burning (2.8%) were the main pollution sources of PM 2.5 in Zhengzhou. SOA and coal combustion contributed more in winter and fugitive dust contributed more in spring, followed by autumn. Biomass burning contributed more in spring and autumn. The urban sites JCZ and ZZU and the characteristic site HKG near the airport were more affected by traffic sources (16.9%, 16.2%, and 16.0%, respectively) than the other sites. The impact of biomass burning on the non-urban sites XM and HKG was slightly larger (both 2.7%), and the contribution of coal combustion to the suburban site XM was higher (16.8%). Owing to the construction around ZZU, the loading of fugitive dust at ZZU was higher than that at other sites. Comparing the results of the two-year autumn and winter, the contribution of SOA, traffic, and industry increased in the autumn and winter of 2018, whereas the contribution of secondary inorganic salts, coal combustion, and biomass burning decreased and the contribution of fugitive dust in winter also decreased. The results showed that the control strategies in autumn and winter had significant effects on the primary sources, including fugitive dust, coal combustion, and industry, and SOA precursor volatile organic compounds should be targeted for further pollution control.

In this study, PM 2.5 samples were collected synchronously at Gaoxin and Linxiao in Luoyang City during autumn and winter (4 October 2018 to 30 January 2019). Sixteen priority polycyclic aromatic hydrocarbons (PAHs) associated with fine particulate matter were analyzed by gas chromatography mass spectrometry (GC-MS). The concentrations and composition characteristics of the PAHs on clean and polluted days were studied. Diagnostic ratio analysis and principal component analysis (PCA) were used to identify the emission sources of PM 2.5 -bound PAHs and the equivalent carcinogenic concentration of benzo[a]pyrene (BaP) and incremental lifetime cancer risks (ILCRs) model were applied to evaluate health risks. During the sampling period, the concentrations of PAHs at Gaoxin and Linxiao ranged 24.33-90.26 ng·m -3 and 23.81-76.99 ng·m -3 , respectively. With the increase in PM 2.5 pollution, PAH concentrations increase significantly (the mean PAH concentration on polluted days was approximately 1.3 times higher than during clean days). PAH profiles at different polluting levels were similar; 4-ring PAHs (43%-48%) > 5-6 ring PAHs (32%-35%) > 2-3-ring PAHs (20%-22%). Diagnostic ratios and PCA demonstrated that PAHs in the study area were mainly derived from combustion sources including coal combustion, biomass burning, and motor vehicle emissions. The coal combustion was the main pollution source in the study area (clean days=49.28%-56.38%, polluted days=49.44%-60.60%). The results of the equivalent carcinogenic concentration of benzo[a]pyrene (BaP) and ILCR model revealed that the human health risk on polluted days was higher. Moreover, the cancer risks from adult exposure to PAHs were higher than those child exposure, which has an acceptable level of risk (<10 -6 ).

To evaluate the health benefits brought about by air environmental treatment and determine the main drivers of health risk, we calculated the health and economic benefits attributed to PM 2.5 control in Eastern and Central China from 2013 to 2017 by combining PM 2.5 concentrations with a human activity enhanced exposure-response model. The relative contributions of changes in four factors related to the PM 2.5 health burden were also quantified, namely total population, population aging, baseline mortality rates, and ambient exposure. The results show that the population weighted PM 2.5 concentration decreased by 28.73% and the proportion of the population exposed to annual PM 2.5 concentrations lower than or equal to 35 μg·m -3 increased from 11.23% to 27.91% across the study area during this period. Avoided deaths were decreased to 14.43%, which equates to avoided economic losses of approximately 559 billion RMB. If PM 2.5 concentration meets the Chinese Ambient Air Quality Standard Grade Ⅱ (35 μg·m -3 ) or Grade Ⅰ (15 μg·m -3 ), or the World Health Organization Air Quality Guidelines (AQG) standards (10 μg·m -3 ), a 8.22%, 55.05%, and 79.36% reduction in the total deaths could be achieved in the base year (2017) with equivalent total economic benefits of approximately 319, 2137, and 3081 billion RMB, respectively. Total population, population aging, baseline mortality rates, and PM 2.5 concentrations contributed -2.69%, -12.38%, 1.66%, and 14.57% to PM 2.5 -related deaths. Overall, during the study period, the reduction in PM 2.5 concentrations has been the main factor contributing to the reduction in the public health burden. China has implemented significant air pollution control measures; however, the health burden associated with high PM 2.5 concentrations in densely populated areas is still extremely high, requiring an aggressive air pollution control strategy.

Based on online monitoring data of air quality and meteorological parameters, the long-term variations, spatial differences, and meteorological influencing factors of ground-level ozone (O 3 ) pollution in Zhengzhou were studied. In addition, the transport pathways and potential source regions of O 3 were investigated. The results show that surface O 3 concentrations at the city station in Zhengzhou City increased significantly during the period 2014-2018 ( P <0.05) with a growth rate of 15.50 μg·(m 3 ·a) -1 , and the timespan of exceeding pollutant standards was extended. The monthly O 3 variations showed an "M" pattern with the seasonal maximum in summer. The diurnal O 3 variations showed a "single-peak" pattern with a diurnal concentrations peak at 15:00-16:00, while the diurnal peak at the rural station was relatively high (130.94 μg·m -3 ). At the urban station, the exceedance probability of O 3 concentrations was relative high when hourly temperature ( T ) exceeded 23℃, relative humidity (RH) was less than 65%, wind speed (WS) ranged 2.0-4.0 m·s -1 , and wind direction was southeast or northeast. Based on the multivariate linear fitting of impact factors on O 3 , the main controlling factors at the city and industrial sites were also identified as T and RH compared to T and WS at the traffic and suburb sites. Back trajectory analysis and potential sources of O 3 during different seasons were significantly different, with the dominant transport trajectories during spring and summer being short-distance and slow-moving airflows from the south and northeast; autumn and winter were characterized by long-distance and quick-moving airflows from the northwest. The high O 3 concentrations observed in summer were mainly affected by local photochemical formation and regional transport from Hebei, Shandong, and Anhui Provinces.

To study the characteristics of PM 2.5 pollution and the potential sources of its main components in the central plain urban agglomeration in autumn and winter, PM 2.5 samples were collected continually in the four typical cities of Zhengzhou, Luoyang, Anyang, and Xinxiang from October 2018 to January 2019. X-ray fluorescence spectrometry, carbon analysis methods, and ion chromatography were used to determine 18 kinds of inorganic elements, organic carbon (OC)/elemental carbon (EC), and 9 kinds of water-soluble inorganic ions. According to the daily PM 2.5 concentration, three pollution levels were divided, and the comparative analysis for the spatial and temporal variation of PM 2.5 and its main components, i.e., NO 3 - , OC, and 18 kinds of inorganic elements, were studied via the calculation of the nitrogen oxidation rate (NOR), secondary organic carbon (SOC), and enrichment factor. The emission sources and their contribution rates of PM 2.5 pollution level in the four cities were calculated by a chemical mass balance (CMB) model; the potential pollution sources of PM 2.5 and its main components, NO 3 - and OC, in the four cities were analyzed by a backward trajectory model (HYSPLIT) and potential source contribution factor method (PSCF). The results showed that the means of PM 2.5 in Zhengzhou, Luoyang, Anyang, and Xinxiang were (82.1±45.5), (84.7±39.8), (96.8±46.1), and (81.1±36.6) μg·m -3 , respectively, during the sampling period, and the maximum daily mean values were 3.3, 2.6, 3.0, and 2.3 times, respectively, of the Chinese national secondary standard; the main components of PM 2.5 in the four cities were NO 3 - and SOC, and the concentration of NO 3 - , the ratio of NO 3 - /EC, and NOR all increased significantly with the rising of pollution levels, generally showing that the mean values of NO 3 - /EC and NOR of Zhengzhou and Luoyang were a little higher than those of Anyang and Xinxiang; the concentration of SOC, the proportion of SOC in OC, and the ratio of SOC/EC all increased with the rising of pollution levels. From the concentration and enrichment degree of inorganic elements, As was the highest in Zhengzhou; Mn and Fe were the highest in Luoyang; Zn, Ni, and Cr were the highest in Anyang; and Cu and Pb were the highest in Xinxiang. Secondary nitrate, secondary sulfate, organic matter, coal combustion, motor vehicle, dust, biomass burning, and industrial processes were the main PM 2.5 pollution sources in the four cities, with the highest contribution rate of secondary nitrate in Zhengzhou (37.7%), the highest contribution rate of vehicle sources in Xinxiang (14.1%), and a relatively high contribution rate of industrial process source in Luoyang (7.0%) and Anyang (6.8%). The northwest direction of airflow contributed 51.6%, 49.2%, 49.6%, and 46.3% of the total airflow in Zhengzhou, Luoyang, Anyang, and Xinxiang, respectively. From the potential pollution area of each city, the Zhengzhou area was mainly concentrated in Henan province, the Luoyang area was mainly concentrated in the south of Henan province and Fen-wei plain, and the Anyang and Xinxiang areas were mainly concentrated in Henan province and the Beijing-Tianjin-Hebei transport belt. The pollution levels of OC in Anyang and Xinxiang were also affected by the northwest Anhui, southwest Shandong, southeast Shanxi, and north Shaanxi.

The aim of this study was to fully understand the pollution characteristics and sources of PM 2.5 in Zhengzhou, and to investigate the differences in four seasons and between urban and suburban areas. At the Zhengzhou environmental monitoring center (urban areas) and Zhengzhou University (suburban areas), 1284 environmental PM 2.5 samples were collected in the four seasons of 2018. The concentrations of nine kinds of inorganic water-soluble ions, organic carbon, elemental carbon and 27 kinds of elements, were measured by ion chromatography, carbon analyzer, and X-ray fluorescence spectrometry, respectively. Enrichment factors (EF), index of geoaccumulation ( I geo ), potential ecological risk index (RI), chemical mass balance model (CMB), backward trajectory, and potential source contribution function were the methods used to study the chemical component characteristics and source differences of PM 2.5 in different seasons in the urban and suburban areas of Zhengzhou. The results showed that the annual average PM 2.5 concentration at the Zhengzhou environmental monitoring center and Zhengzhou University sites reached (59.7±24.0) μg·m -3 and (74.7±13.5) μg·m -3 , respectively. The PM 2.5 concentration at the suburban point was higher than at the urban point with the exception of winter, and the seasonal mean concentration decreased in the order of winter > autumn > spring > summer. Compared with the urban areas, the suburban areas were more affected by crustal substances in spring, and the concentrations of all components were higher in summer and autumn than the urban areas. Nevertheless, urban areas were more affected by coal burning sources and motor vehicle sources in winter. The component analysis results showed that the influences of soil dust and building dust were greater in the suburbs in spring than in the urban areas. In autumn, the suburbs were more affected by biomass sources than the urban areas, while the urban areas were more affected by building dust than were the suburbs. The concentrations of Cu, As, Zn, Pb, and Sb were strongly influenced by anthropogenic sources, and the enrichments of Zn, Cu, As, and Pb in urban areas were greater than in the suburbs. In addition, Zn, Cu, As, and Pb exhibited potential ecological risks. The outcomes of the CMB model showed that dust sources, secondary sulfate, secondary nitrate, and coal burning sources contributed significantly to PM 2.5 concentrations in spring, summer, autumn and winter, respectively. The contributions of secondary pollution sources (secondary organic aerosol, secondary sulfate, and secondary nitrate) and motor vehicle sources to urban areas were higher than to suburban areas, and the influences of biomass sources in autumn and winter were significantly higher than in spring and summer and urban areas. The backward trajectory results indicated that the local PM 2.5 concentration was affected by distant transmission from the northwest except in summer, was affected by neighboring provinces in the east in four seasons, and was affected by transmission from the south, with the exception of winter. Furthermore, the consequences of potential sources demonstrated that the local PM 2.5 concentration was mainly affected by the potential areas in Henan province and its boundary with neighboring provinces.

During the National Traditional Games of Ethnic Minorities (NTGEM) 2019, air quality in Zhengzhou was analyzed to evaluate the impact of pollution prevention and control measures on Zhengzhou. Ground-observed meteorological and pollutant data as well as the chemical compositions of volatile organic compounds (VOCs) were investigated. The results showed that the six parameters of pollutants in the safeguard period in 2019 indicated a downward trend as compared with that during the same time in 2018, and the average concentrations of PM 2.5 and PM 10 were decreased by 16.2% and 25.1%, respectively. However, the average concentration of O 3 was only reduced by 3.7%. The daily proportions of primary pollutants of O 3 increased to 90% during the NTGEM, and the ozone pollution was severe in this period. Meanwhile, the concentration of total volatile organic compounds (TVOCs) in the safeguard period was 26.21×10 -9 , which was significantly lower than that during the historical period. Six emission sources of the VOCs were identified using PMF model, including vehicle exhaust (28%), LPG evaporation (21%), combustion source (16%), industrial emissions (15%), solvent utilization (15%), and biogenic VOCs (5%). During the NTGEM period, the control of combustion sources and industrial sources was evident.

In this study, according to the activity levels of volatile organic compounds (VOCs) sources and source profiles, a 2016-based inventory of the speciation emission of the VOCs was established and the ozone formation potential (OFP) were estimated in Zhengzhou. The results showed that the total VOCs emission in Zhengzhou in 2016 was 96215.3 t. The highest emission source was on-road mobile source (29.7%) followed by solvent use sources (28.1%). The species that contributed the highest emission was alkanes (29.8%) followed by aromatics (29.0%). The OFP in Zhengzhou in 2016 was 341291.0 t with the highest contributing source as on-road mobile (30.5%) followed by solvent use source (28.8%). Moreover, the light duty gasoline vehicle, use of interior wall coatings, vehicle surface coating, gas station loading and unloading, and manufacture of non-metallic mineral were the major secondary emission sources of OFP, which needed to be controlled for reducing ozone pollution in Zhengzhou. For VOCs species group, the higher contribution groups were aromatics (42.8%) and alkenes (38.9%). The sources that produced m/p -xylene, propylene, ethylene, and other species should be paid more attention.

In this study, volatile organic compound (VOC) species were measured at an urban site in Zhengzhou from January 3 to 23, 2019, to investigate the composition, variation characteristics, sources, and effects on secondary organic aerosol (SOA) formation of VOCs at different pollution levels. Results showed that oxygenated VOCs and alkanes were the main components of VOCs, while ethyl acetate and acetone were the most abundant species. During the process from clean days to heavy pollution days, the mixing ratio of VOCs approximately doubled, and the mixing ratios of most species continued to increase as the pollution level increased. Based on the positive matrix factorization (PMF) model, during the observation period, VOCs mainly originated from vehicle emissions, industrial emissions, combustion sources, solvent utilization, and liquefied petroleum gas (LPG) utilization. There were significant differences in the source contribution at different pollution levels, and the contributions of industrial emissions and solvent utilization during the heavy pollution days increased to 9 times and 3 times that of the clean days, respectively. With respect to the SOA formation potential (SOA p ), aromatics were the component that contributed the most, and toluene and m/p -xylene were the species that contributed the most, while solvent utilization was the greatest source contributor. During the heavy pollution period, the total SOA p increased to approximately 2.6 times that of clean days. There is a great need to reduce winter haze pollution in Zhengzhou by strengthening the control of aromatic emissions and related sources such as solvent utilization.

To evaluate the effect of the implementation of emission reduction measures and the improvement in air quality during the National Traditional Games of Ethnic Minorities in Zhengzhou, a series of online instruments were used to continuously observe air pollutants and components of PM 2.5 from August 5 to September 30, 2019. Three cases, including before emission reduction (August 5-24), during emission reduction (August 25 to September 18), and after emission reduction (September 19-30), were classified by the implementation of control measures. The results show that the growing concentration of PM 2.5 after the cancellation of emission abatement measures (11.7 μg·m -3 ) was greater than that during the emission reduction (2.3 μg·m -3 ) compared to the PM 2.5 concentration before emission reduction. This thus indicates that the control measures have a significant effect on reducing particulate matter. The main components of PM 2.5 were organic matter, nitrate, ammonium, sulfate, and crustal elements. Compared to the proportion of components in PM 2.5 before and during the control periods, organic matter and nitrate increased by 3.9% and 0.9%, respectively, while sulfate, ammonium, and crustal elements decreased by 1.1%, 1.9%, and 2.2%, respectively. The results of source appointment by positive matrix factorization show that secondary sulfate, secondary nitrate, secondary organic aerosols, vehicular emissions, industrial emissions, dust, and coal combustion are the main sources of PM 2.5 . Emission abatement measures reduced the contributions of primary sources such as dust, coal combustion, and industry by 8.3%, 8.2%, and 8.1%, respectively. In contrast, the contributions of secondary organic and nitrate aerosols increased during the control periods, which suggested that the control measures implemented in Zhengzhou had a weaker emission reduction effect on nitrogen oxide and volatile organic compounds than on primary sources of PM 2.5 .

Ambient volatile organic compounds (VOCs) were determined by GC 5000 online gas chromatography in the urban site of Zhengzhou from April 15 to May 15, 2018. Based on chemical composition analysis, in this study, the concentrations, ozone formation potential (OFP), and source apportionment were studied. The results show that the averaged volume fraction of VOCs in Zhengzhou during spring was 40.26×10 -9 , which was 23% higher on polluted days (44.12×10 -9 ) than on non-polluted days (35.82×10 -9 ). The contribution of VOC species to OFP was in the order: alkenes > aromatics > alkanes > alkyne. The five factors identified by the PMF model were liquefied petroleum gas (LPG) volatilization sources (66.05%), motor vehicle exhaust sources (47.39%), industrial solvent sources (37.51%), fuel combustion sources (37.80%), and biogenic sources (11.25%). The contributions of LPG volatilization sources and biogenic sources on polluted days were higher by 22.92% and 68.50% than on non-polluted days, respectively.

The spatio-temporal characteristics of ozone (O 3 ) pollution in the Henan Province in 2017 and its relationship with particulate matter, precursors, and meteorological factors were studied using the data obtained from the air quality monitoring station and national baseline ground climate station. Results showed that the annual mean O 3 concentration of a maximum daily 8-h average (MDA8) was 108 μg·m -3 in the Henan Province with the trend of summer > spring > autumn > winter. Different levels of O 3 were observed in various cities. The number of days exceeding the standard was up to 88 days in Anyang, while the lowest was found in Xinyang with 17 days. The most severe of O 3 pollution occurred during late spring and early summer. During this period, the average monthly concentration of O 3 MDA8 was above 140 μg·m -3 , and peaked in June. The qualitative and quantitative analysis showed that the monthly average concentration of O 3 MDA8 was negatively correlated with particulate matter, and the O 3 hourly concentration was also negatively correlated with CO and NO 2 . The O 3 MDA8 concentration and meteorological factors (sunshine duration, temperature, rainfall, visibility, humidity, and wind speed) had different correlations during different seasons and various cities.

This study uses the national ambient air quality station data of Anyang City from 2014 to 2017 to analyze the characteristics and variation rules of ozone (O 3 ) in different functional zones (urban, suburban, and industrial), and also studies meteorological influencing factors and potential source distributions of O 3 pollution. The results showed that the O 3 concentration in Anyang increased significantly between 2014 and 2017, whereby pollution started to advance from 2015 and the earliest occurrence of an O 3 pollution episode was in April 2017. The 90 th percentile and average concentrations of O 3 in the industrial zone increased at faster rates (average annual growth of 16.0 μg·m -3 and 13.0 μg·m -3 , respectively) than of those in the urban and suburban zones. The O 3 concentrations at the suburban site showed the fastest increase of all zones at the 5 th percentile (average annual growth of 13.2 μg·m -3 ). The monthly variation in O 3 concentrations in Anyang showed an "M" pattern that varied spatially depending on the zone. Temperature played a leading role in O 3 concentrations; air temperatures >23℃, relative humidity <58%, and a wind speed of 5 m·s -1 in a south-southwesterly direction were closely related to the occurrence of high O 3 concentrations. Potential sources of O 3 in different seasons were significantly different, and mainly distributed in southern Hebei, northern Hubei, and northern Shenyang in summer. The first heavy O 3 pollution day in the studied period was in May 2017, at which time the highest O 3 concentration was found in the industrial zone (up to 405 μg·m -3 ). This heavy O 3 pollution episode was related to the continuous high temperature that was caused by the transfer of a dry, hot air mass in western China.

The three typical carbon enterprises in Zhengzhou were selected as research targets, and the emission characteristics of volatile organic compounds (VOCs) and their ozone formation potential (OFP) in different functional areas were studied. The US Environmental Protection Agency (EPA) health risk assessment model was used to evaluate the health risks of VOCs emitted by the carbon industry. The results showed that the concentration of VOCs in the production areas of the three research enterprises was between 89.77-964.60 μg·m -3 , and the management area was between 51.46-121.59 μg·m -3 . Naphthalene and carbon disulfide were at the highest concentrations in the carbon plants. The ozone formation potential of VOCs in the production area was between 75.42-1416.73 μg·m -3 , and in the management area was between 65.32-202.42 μg·m -3 , mainly from the contribution of aromatic hydrocarbons and olefins. The carcinogenic health risk (Risk) of VOCs in the production area was 3.5×10 -5 -2.8×10 -3 , and in the management area was 2.0×10 -5 -9.4×10 -5 , which was higher than the maximum acceptable level recommended by the EPA (10 -6 ). The non-carcinogenic health risk index (HI) of the VOCs in the production area was 3.2-1.4×10 2 , and in the management area was 4.3×10 -1 -3.8, except for the management area of the first enterprise, which was greater than 1, which may expose the workers. These health factors cause cancer and non-carcinogenic hazards.

To study the pollution characteristics of atmospheric heavy metal elements in a living area of Zhengzhou City, assess the potential ecological risks, and determine risks to resident health in this city, the Wuhan Tianhong TH-16A Airborne Particles Intelligent Sampler was used to collect atmospheric PM 2.5 in Zhengzhou City. The mass concentrations of 17 metal elements were analyzed by ambient air determination of inorganic elements by ambient particle matter wavelength dispersive X-ray fluorescence spectrometry. The source of heavy metals was analyzed by the enrichment factor method and principal component analysis. The ecological risk index method and the US Environmental Protection Agency's health risk assessment method were used to evaluate the potential ecological risks and residents' health risks from Cr, Cd, Cu, Zn, Ni, Pb, As, and other elements. The results showed that metals with higher enrichment factor values were Cd, Sb, Pb, and As, and Cd had the highest enrichment factor value. The sources of metal elements in a living area of Zhengzhou City were mainly crust/burning coal, fuel, garbage burning, metallurgical dust, and vehicle emission. The single factor potential ecological hazard index values of Cd, Pb, Zn, As, Cu, Ni, and Cr were 70420.2, 255.3, 204.6, 71.5, 36.9, 24.0, and 5.1, respectively. Cd, As, and Cr in a living area of Zhengzhou City posed a cancer risk, and Cd was the most harmful. Mn had a non-carcinogenic risk.

Ambient volatile organic compounds (VOCs) samples were collected at five sites in Zhengzhou during the spring of 2018. VOCs concentrations, the ozone formation potential (OFP), the aerosol formation potential (AFP), and source apportionment using a positive matrix factorization (PMF) model were studied based on chemical composition analysis. The results showed that the averaged concentration of VOCs in Zhengzhou during spring was (30.66±13.60)×10 -9 , of which the proportion of alkanes was the highest (35.3%) followed by oxygenated VOCs (OVOCs, 25.3%), halocarbons (24.1%), aromatics (10.0%), and alkenes (5.2%). The total OFP was 195.53 μg·m -3 and the contributions of alkanes, alkenes, aromatics, halocarbons, and OVOCs were 25.6%, 17.8%, 38.9%, 5.8%, and 11.9%, respectively. The total AFP was 0.95 μg·m -3 with an 87.6% contribution from aromatics and 12.4% from alkanes. The correlation between major species showed that pentane, isopentane, benzene, and toluene in Qinlinglu (QLL) site and Jingkaiqu (JKQ) site were greatly influenced by motor vehicles, but these were mainly influenced by combustion sources in Zhengzhou University (ZZU) site. The five factors that were identified by the PMF model were vehicle and liquefied petroleum gas (LPG) volatilization source (30.5%), solvent coating source (27.3%), industrial process source (22.1%), aging air mass (14.4%), and biogenic source (5.7%).

Online monitoring data of atmospheric pollutants and meteorological parameters in Zhengzhou from 2014 to 2017 were collected to analyze the concentration levels, seasonal variations, and the ratio characteristics of atmospheric pollutants, as well as the effects of meteorological conditions on these pollutants. Results show that the annual average concentrations of PM 2.5 and PM 10 in the four years were (88±49.8), (95.8±60.2), (78.6±70.3), and (72.0±53.5) μg·m -3 , and (158.5±65.3), (167.7±82.6), (144.5±91.5), and (132.7±70.3) μg·m -3 , respectively, and were approximately two times higher than the grade Ⅱ annual limits set by China. Fuel combustion and the formation of secondary pollutants were the main sources of PM 2.5 in Zhengzhou. Moreover, the contribution of coal combustion was found to be decreased, while that of traffic sources increased year by year. Low wind speed, high humidity, and reduced precipitation are important meteorological factors that contribute to serious air pollution. In addition, the Potential Source Contribution Function and the Concentration-Weighted Trajectory were used to analyze the potential sources, and the contribution of these sources to the distribution of PM 2.5 . The potential source areas of PM 2.5 were mainly distributed in neighboring cities and provinces, such as Shanxi, Shaanxi, Hubei, Shandong, and Hebei. The short-distance transmission was found to contribute significantly to the mass concentration of PM 2.5 .

In order to explore the pollution characteristics of water-soluble ions in PM 2.5 in Zhengzhou, high time resolution and continuous observation of water-soluble inorganic ions in PM 2.5 was conducted from December 1, 2017, to November 30, 2018, in Zhengzhou. The results showed that during the observation period, the average concentration of total water-soluble ions in Zhengzhou was 42.7 μg·m -3 . The order of mass concentration of each ion, from large to small, was as follows:NO 3 - (17.7 μg·m -3 ), SO 4 2- (10.2 μg·m -3 ), NH 4 + (9.0 μg·m -3 ), Cl - (2.3 μg·m -3 ), K + (1.3 μg·m -3 ), Na + (1.3 μg·m -3 ), Ca 2+ (0.8 μg·m -3 ), and Mg 2+ (0.1 μg·m -3 ). The mass concentration of total water-soluble ions was the highest in winter, slightly higher in autumn than in spring, and lowest in summer. The diurnal variation in single peak distribution was observed across the whole year in spring, summer, and autumn, while there was no significant diurnal variation in winter. The mass concentration of secondary inorganic ions (SO 4 2- , NO 3 - , and NH 4 + ) accounted for 43.8% of PM 2.5 , mainly in the form of (NH 4 ) 2 SO 4 and NH 4 NO 3 . There was a large degree of secondary transformation throughout the observation period; relative humidity had a significant influence on the sulfur oxidation rate, and temperature had a significant influence on the nitrogen oxidation rate. During the observation period, there was a good correlation between secondary ions, and K + showed a good correlation with Mg 2+ and Cl - . The main source of the secondary ions was the secondary conversion of gaseous pollutants. Mg 2+ and Ca 2+ were derived from soil dust and construction dust. K + was one of the main biomarkers of biomass combustion. Na + was mainly derived from sea salt and soil dust, and Cl - was derived not only from sea salt but also biomass and fossil fuel combustion. The results of principal component analysis showed that the water-soluble ions in PM 2.5 in Zhengzhou were mainly affected by secondary transformation, combustion sources, and dust emission from soil or building construction.

Based on the annual average concentration values, the health effects and health benefits as well as 95% confidence intervals of PM 10 and PM 2.5 pollution control from 2014 to 2016 in Zhengzhou were evaluated by applying the Poisson regression relative risk model. Results showed that the health benefits of PM 10 pollution control were 18.18 billion RMB (15.04, 21.12), 24.25 billion RMB (20.25, 27.94), and 20.62 billion RMB (17.33, 23.92), which accounted for 2.7%, 3.3%, and 2.5% of the GDP of Zhengzhou, respectively, in 2014-2016. The health benefits of PM 2.5 pollution control were 17.88 billion RMB (14.37, 21.16), 21.65 billion RMB (17.46, 25.53), and 17.25 billion RMB (13.78, 20.55), which accounted for 2.6%, 3.0%, and 2.1% of the GDP of Zhengzhou, respectively, in 2014-2016. After the PM 10 and PM 2.5 pollution was controlled, the number of urban beneficiaries was higher than that of rural areas, and acute bronchitis beneficiaries were higher than the beneficiaries of other health end-points. For chronic bronchitis, adults benefited more than children, while the opposite occurred for asthma. In this study, chronic bronchitis had the highest health benefit, followed by asthma, and outpatient and inpatient setting had the lower health benefits.

Samples of particulate matter from flue gas emissions of typical brick, cement, and firebrick industrial kilns in Zhengzhou City were collected by dilution channel systems. Cr, Mn, Fe, Co, Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, V, Ni, Cu, Zn, Ga, As, Se, Sr, Cd, Sb, Sn, Ba, and Pb were analyzed. The emission factors (EFs) and emissions of inorganic elements of PM 2.5 from kilns flue gases of three industries in Zhengzhou City during 2016 were calculated. A grid list of 1 km×1 km was also established. The results show that the highest concentration of total inorganic elements was in the firebrick industry, corresponding to(609.97±490.97) μg·m -3 . The concentration of inorganic elements in the three industries accounted for 34%-54%, 27%-42%, and 23%-53% of PM 2.5 . The inorganic elements emitted from industrial kilns in brick and cement industries were mainly crust elements, and the highest concentration elements were Cl and Al. The inorganic elements emitted by industrial furnaces in the firebrick industry were mainly heavy metals, and the highest concentration element was Pb. The coefficient of divergence (CD) of inorganic elements in brick and cement industry was 0.389, that is slightly different. The CD between cement and refractory industry was 0.732, which represents a significant difference between inorganic element emissions. In 2016, the emissions of Pb, S, Zn, Cl, K, As, Fe, Si, Cr, Al, Na, and Ca in PM 2.5 from major industrial furnaces in Zhengzhou City were 919.0, 793.1, 124.7, 378.6, 82.6, 12.2, 60.4, 145.4, 7.4, 86.6, 15.8, and 111.4 kg·a -1 , respectively. Heavy metal emission in the Xinmi area was the highest, representing a high health risk.

To investigate the characteristics of PM 2.5 emissions in Anyang, the PM 2.5 emission inventory from industrial sources of Anyang in 2016 was established based on reasonable estimation method and emission factor. The spatial distribution analysis was carried out using Geographic Information System (GIS) technology. Based on the industry's typical ultra-low emission transformation and coal pressure reduction requirements, three scenarios were set to estimate the PM 2.5 emission reduction potential of industrial sources from Anyang in 2020. The results show that the total PM 2.5 emissions from industrial sources in Anyang was 81071.13 t in 2016. Non-ferrous metallurgy, iron and steel, and building materials industries were the main contributing sources, accounting for 45.43%, 25.74%, and 18.00% of the total emissions, respectively. The emission differences between towns and within Anyang were outstanding. The PM 2.5 emissions were mainly concentrated in urban areas, Linzhou City, and Anyang County. The emissions in Anyang City were most prominent, while the emission intensity differences between the four jurisdictions of Anyang City were even greater. In the three different control scenarios, the potential for PM 2.5 emission reduction in Anyang in 2020 was estimated to be 398.72, 11623.87, and 14072.27 t, accounting for 0.49%, 14.34%, and 17.22% of the total industrial source emissions in 2016, respectively. It can be concluded that Anyang has a great potential for PM 2.5 emission reduction. In addition, ultra-low emission reform and coal pressure reduction can be of great significance for PM 2.5 emission reduction in Anyang.

In this study, meteorological and air mass concentration data of Zhengzhou from December 2017 to February 2018 (winter) were used to quantify the influence of meteorological factors on the PM. The Hybrid Single-Particle Lagrangian Integrated Trajectory model was used to analyze the 48-hour backward trajectories and the cluster method was applied to classify the airflow backward trajectory. Moreover, the potential source contribution function and concentration-weighted trajectory analysis were applied to evaluate the transport pathways and sources of PM 2.5 in Zhengzhou. The results show that the heavy pollution in Zhengzhou during winter is mainly due to the low wind speed, high relative humidity, and low precipitation. The cluster analysis revealed that up to 60% of the back trajectories came from the northwest and 25.56% of the back trajectories came from the Beijing-Tianjin area. The airflow trajectories from the south and east account for 7.5% and 6.1% with higher PM 2.5 concentrations. The main potential sources of PM 2.5 in Zhengzhou during winter are located in Beijing-Tianjin-Hebei air pollution transmission channel cities including Jiaozuo, Kaifeng, Xinxiang, Hebi, Puyang, Anyang, Handan, and Xingtai. The adjacent provinces, including Shanxi, Hubei, and Anhui, also have great influence on the PM 2.5 in Zhengzhou.

To study the compositional characteristics of atmospheric particulates with different particle sizes in the central city of Zhengzhou, China, a Tisch graded impact sampler was used to sample atmospheric particulates in summer and autumn. The mass concentrations of water-soluble inorganic ions, including anions (Cl - , F - , NO 3 - , and SO 4 2- ) and cations (Na + , Ca 2+ , NH 4 + , K + , and Mg 2+ ) were measured by ion chromatography, and the online ion chromatography-based analyzer MARGA monitored the real-time concentration of particulate nitrate. The results showed that the average concentration of water-soluble ions in Zhengzhou City was (70.9±52.1) μg·m -3 during the sampling period, and the order of water-soluble ion mass concentrations was NO 3 - > SO 4 2- > NH 4 + > Ca 2+ > Na + > Cl - > Mg 2+ > K + > F - ; NO 3 - , SO 4 2- , and NH 4 + accounted for 79.9% of total water-soluble ions. The NO 3 - concentration was mainly concentrated in the 0.65-3.3 μm particle size segment, despite the SO 4 2- concentration being concentrated in the ≤ 1.1 μm particle size segment in autumn or summer. Both NO 3 - and SO 4 2- had a bimodal distribution in summer and autumn and were mainly distributed as fine particles. NH 4 + showed seasonal variation with a bimodal distribution in summer and a unimodal distribution in autumn. Zhengzhou City had serious ozone pollution in summer, and O 3 and NO 3 - showed the "staggered peak" phenomenon, indicating photochemical reactions in the atmosphere. In autumn, water-soluble inorganic ion concentration in particulate matter was high, and the ratio of[NO 3 - ]/[SO 4 2- ] was higher than 0.5. The mobile source is an important source of particles. NOR and SOR peaks were on the 1.1-2.1 μm particle size segment in summer, whereas those in the 0.65-1.1 μm particle size segment occurred in autumn. The sulfur gas-to-grain conversion in summer was larger than that of nitrogen, contrary to the result in autumn.

Based on the best available activity data at a city level from top down and bottom up methods, a 2013-based emission inventory of NH 3 was established for the Henan Province using an emission factors method. The 3 km×3 km spatial gridded distribution was carried out by using GIS technology. The results showed that the total amount of atmospheric NH 3 emission in Henan Province in 2013 was 1035.3 kt, and the average emission intensity reached levels of 6.4 t/km 2 . Livestock and nitrogen fertilizer applications were the top two emission sources, accounting for 52.71% and 31.53% of the total emissions, respectively. Beef, laying hen, and goats were the main contributors in the livestock category, accounting for 34.98%, 16.63%, and 14.02% of the total emissions, respectively. There were different characteristics between emission source contributions and emission intensities in each city. Nanyang, Zhoukou, Shangqiu, and Zhumadian were the prefecture-level cities with large emissions, accounting for 11.53%, 9.84%, 9.62%, and 9.57% of the total amount in Henan Province, respectively. The NH 3 emission intensities of Puyang and Louhe were larger than those of other cities, reaching up to 10.7 t·km -2 and 10.2 t·km -2 , respectively. The spatial distribution revealed that emissions in the middle eastern region were relatively higher; whereas, the western region emissions were relatively low. The areas with high emissions were concentrated in the plains and densely populated areas.