Temporal and Spatial Patterns of China’s Main Air Pollutants: Years 2014 and 2015

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

To understand the influence of the energy structure (including solid fuel and clean energy) on air pollution, two comprehensive measurement campaigns were conducted in Baoding and Shanghai in late autumn and winter during 2017-2018. The chemical compositions, driving factors, regional transport of pollutants, and potential respiratory disease (RD) health risks of PM_2.5 for Baoding and Shanghai were analyzed. The results showed that the concentration of PM_2.5 in Baoding (156.9 ± 139.8 μg m^-3) was 2.6 times of that in Shanghai (60.9 ± 45.9 μg m^-3). The most important contributor to PM_2.5 in Baoding was organic matter (OM), while inorganic aerosols accounted for major fractions of PM_2.5 in Shanghai. Positive matrix factorization (PMF) results indicated that coal combustion (CC; 39%) accounted for the most in Baoding, followed by secondary aerosols (21%), biomass burning (BB; 20%), industrial emissions (14%), dust (3%), and vehicle exhaust (2%). However, the average contribution in Shanghai followed the order: secondary aerosols (44%), vehicle exhaust (36%), dust (11%), marine aerosols (6%), and BB (3%). The evolution of source contributions at different pollution levels revealed that haze episodes in Baoding and Shanghai were triggered by CC and secondary formation, respectively; however, the air quality on clean days in Baoding and Shanghai was affected mostly by BB and vehicle emissions, respectively. Potential source contribution function (PSCF) results suggested that CC in Baoding was primarily from local emissions, while BB was primarily from local and regional transport. Vehicle exhaust and secondary aerosols in Shanghai were mainly from local emissions and regional transport. The number of RD deaths related to haze episodes in Baoding and Shanghai were 215 (95% CI: 109, 319) and 76 (95% CI: 11, 135), respectively. This research also emphasized the importance of further attention to the usage of coal in Baoding and vehicle emissions in Shanghai.

Development and evaluation of an advanced National Air Quality Forecasting Capability using the NOAA Global Forecast System version 16

A new dynamical core, known as the Finite-Volume Cubed-Sphere (FV3) and developed at both NASA and NOAA, is used in NOAA's Global Forecast System (GFS) and in limited-area models for regional weather and air quality applications. NOAA has also upgraded the operational FV3GFS to version 16 (GFSv16), which includes a number of significant developmental advances to the model configuration, data assimilation, and underlying model physics, particularly for atmospheric composition to weather feedback. Concurrent with the GFSv16 upgrade, we couple the GFSv16 with the Community Multiscale Air Quality (CMAQ) model to form an advanced version of the National Air Quality Forecasting Capability (NAQFC) that will continue to protect human and ecosystem health in the US. Here we describe the development of the FV3GFSv16 coupling with a "state-of-the-science" CMAQ model version 5.3.1. The GFS-CMAQ coupling is made possible by the seminal version of the NOAA-EPA Atmosphere-Chemistry Coupler (NACC), which became a major piece of the next operational NAQFC system (i.e., NACC-CMAQ) on 20 July 2021. NACC-CMAQ has a number of scientific advancements that include satellite-based data acquisition technology to improve land cover and soil characteristics and inline wildfire smoke and dust predictions that are vital to predictions of fine particulate matter (PM_2.5) concentrations during hazardous events affecting society, ecosystems, and human health. The GFS-driven NACC-CMAQ model has significantly different meteorological and chemical predictions compared to the previous operational NAQFC, where evaluation of NACC-CMAQ shows generally improved near-surface ozone and PM_2.5 predictions and diurnal patterns, both of which are extended to a 72 h (3 d) forecast with this system.

Urban air pollution control policies and strategies: a systematic review

A wide range of policies, strategies, and interventions have been implemented to improve air quality all over the world. This systematic review comprehensively appraises the policies and strategies on air pollutants controls enacted in different countries, worldwide. Three databases, Web of Science, PubMed and Scopus, were used for the search. After screening, a total of 114 eligible manuscripts were selected from 2219 documents for further analysis. Selected articles were divided into two categories: (1) articles focusing on introducing the policies and strategies enacted for controlling air pollution in different countries, and (2) articles which focused on different policies and strategies to control one or more specific pollutants. In the former one, urban air pollution control strategies and policies were divided into four categories, namely, general strategies and policies, transportation, energy, and industry. In case of latter category, policies and strategies focused on controlling six pollutants (PM, SO₂, NO₂, VOC_S, O₃ and photochemical smog). The results indicated that, the most common policies and strategies enacted in most countries are pertinent to the transportation sector. Changing energy sources, in particular elimination or limited use of solid fuels, was reported as an effective action by governments to reduce air pollution. Overall, most policies enacted by governments can be divided into three general categories: (a) incentive policies such as implementing a free public transportation program to use fewer private cars, (b) supportive policies such as paying subsidies to change household fuels, and (c) punitive policies such as collecting tolls for cars to enter the congestion charging areas. Depending on the circumstances, these policies are implemented alone or jointly. In addition to the acceptance of international agreements to reduce air pollution by governments, greater use of renewable energy, clean fuels, and low-pollution or no-pollution vehicles such as electric vehicles play an important role in reducing air pollution.

Stronger secondary pollution processes despite decrease in gaseous precursors: A comparative analysis of summer 2020 and 2019 in Beijing

To control the spread of COVID-19, China implemented a series of lockdowns, limiting various offline interactions. This provided an opportunity to study the response of air quality to emissions control. By comparing the characteristics of pollution in the summers of 2019 and 2020, we found a significant decrease in gaseous pollutants in 2020. However, particle pollution in the summer of 2020 was more severe; PM_2.5 levels increased from 35.8 to 44.7 μg m^-3, and PM₁₀ increased from 51.4 to 69.0 μg m^-3 from 2019 to 2020. The higher PM₁₀ was caused by two sandstorm events on May 11 and June 3, 2020, while the higher PM_2.5 was the result of enhanced secondary formation processes indicated by the higher sulfate oxidation rate (SOR) and nitrate oxidation rate (NOR) in 2020. Higher SOR and NOR were attributed mainly to higher relative humidity and stronger oxidizing capacity. Analysis of PM_x distribution showed that severe haze occurred when particles within Bin2 (size ranging 1-2.5 μm) dominated. SO₄^2-_(1/2.5) and SO₄^2-_(2.5/10) remained stable under different periods at 0.5 and 0.8, respectively, indicating that SO₄^2- existed mainly in smaller particles. Decreases in NO₃^-_(1/2.5) and increases in NO₃^-_(2.5/10) from clean to polluted conditions, similar to the variations in PM_x distribution, suggest that NO₃^- played a role in the worsening of pollution. O₃ concentrations were higher in 2020 (108.6 μg m^-3) than in 2019 (96.8 μg m^-3). Marked decreases in fresh NO alleviated the titration of O₃. Furthermore, the oxidation reaction of NO₂ that produces NO₃^- was dominant over the photochemical reaction of NO₂ that produces O₃, making NO₂ less important for O₃ pollution. In comparison, a lower VOC/NO_x ratio (less than 10) meant that Beijing is a VOC-limited area; this indicates that in order to alleviate O₃ pollution in Beijing, emissions of VOCs should be controlled.

Air pollution characteristics, health risks, and source analysis in Shanxi Province, China

China is confronting an unprecedented air pollution problem. This study discussed the characteristics of air pollution and its risks on human health and conducted source analysis combined with local development in Shanxi Province in 2016 and 2017. Results demonstrated that the air pollution situation in Shanxi was deteriorating, with Taiyuan, Yangquan, Changzhi, Jincheng, Jinzhong, and Linfen being heavily polluted districts. Particulate matter (PM) was considered the major pollutant, but nitrogen dioxide and ozone showed a dominant trend recently. Furthermore, the health risks evaluated on the basis of a comprehensive air quality index (AAQI) and an aggregated risk index revealed a relatively high-risk level in Shanxi. Among the pollutants, the largest contributor was PM, followed by sulfur dioxide and ozone. Southern Shanxi had the largest pollution level and health risks, whereas Datong was the least polluted region. Source analysis suggested that the main driving forces of air pollution, besides natural factors, were urbanization, population size, civil vehicles, coal-based heavy industries, and high-energy consumption. Therefore, strengthening urban greening, vigorously adjusting and optimizing the industrial structure, and formulating a multi-domain cooperative control regime on air pollution, especially PM and ozone, should be promoted.

Air Quality Levels and Health Risk Assessment of Particulate Matters in Abuja Municipal Area, Nigeria

This study focuses on assessing the health risk by particulate matter (PM) inhalation within the Abuja municipal area, Nigeria. Particulate matters (PM2.5 and PM10), HCHO and VOCs were collected by A handheld portable smart air quality detector BR-SMART-126. A hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model for backward trajectory was applied to tract the air flow (transportation) and potential sources. Health risk was estimated by comparing with the air quality index (AQI) stipulated by the World Health Organization (WHO). The result shows that the daily averaged concentrations of PM2.5 varied from 15.30 µg/m3 to 70.20 µg/m3. The top four most-polluted locations (Locations 10, 14, 17 and 18) of the twenty locations were found to be above the acceptable (25 µg/m3) AQI limit stipulated by WHO, which all fell far under the unhealthy AQI value index level. In general, business/commercial locations had the highest PM2.5 level followed by transport/market, offices/mixed use and residential. The results from the backwards trajectories show that the source of local particles for the four most-polluted locations is long-range air transport originating from the Atlantic Ocean. The results of the health-risk assessment implies that for PM2.5, the AQI varied from 73.2 to 280.8 in this assessment. Based on this, the population of workers within the business location are at health risk based on the relatively poor air quality in these areas—especially location 10 and 17. Based on these findings, it is recommended that the regulatory and enforcement agency needs to develop a more robust monitoring mechanism, regulations and enforcement. Furthermore, there is need for a national drive on renewable energy, clean energy for business/commercial district to help reduce fumes from generators and to form cleaner air initiatives in order to ensure a safe environment to live in as well as reduce particulate matters in the city.

Global and Geographically and Temporally Weighted Regression Models for Modeling PM2.5 in Heilongjiang, China from 2015 to 2018

Objective: This study investigated the relationships between PM_2.5 and 5 criteria air pollutants (SO₂, NO₂, PM₁₀, CO, and O₃) in Heilongjiang, China, from 2015 to 2018 using global and geographically and temporally weighted regression models. Methods: Ordinary least squares regression (OLS), linear mixed models (LMM), geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) were applied to model the relationships between PM_2.5 and 5 air pollutants. Results: The LMM and all GWR-based models (i.e., GWR, TWR, and GTWR) showed great advantages over OLS in terms of higher model R² and more desirable model residuals, especially TWR and GTWR. The GWR, LMM, TWR, and GTWR improved the model explanation power by 3%, 5%, 12%, and 12%, respectively, from the R² (0.85) of OLS. TWR yielded slightly better model performance than GTWR and reduced the root mean squared errors (RMSE) and mean absolute error (MAE) of the model residuals by 67% compared with OLS; while GWR only reduced RMSE and MAE by 15% against OLS. LMM performed slightly better than GWR by accounting for both temporal autocorrelation between observations over time and spatial heterogeneity across the 13 cities under study, which provided an alternative for modeling PM_2.5. Conclusions: The traditional OLS and GWR are inadequate for describing the non-stationarity of PM_2.5. The temporal dependence was more important and significant than spatial heterogeneity in our data. Our study provided evidence of spatial-temporal heterogeneity and possible solutions for modeling the relationships between PM_2.5 and 5 criteria air pollutants for Heilongjiang province, China.

Spatio-Temporal Variation Characteristics of PM2.5 in the Beijing-Tianjin-Hebei Region, China, from 2013 to 2018

Air pollution, including particulate matter (PM_2.5) pollution, is extremely harmful to the environment as well as human health. The Beijing–Tianjin–Hebei (BTH) Region has experienced heavy PM_2.5 pollution within China. In this study, a six-year time series (January 2013–December 2018) of PM_2.5 mass concentration data from 102 air quality monitoring stations were studied to understand the spatio-temporal variation characteristics of the BTH region. The average annual PM_2.5 mass concentration in the BTH region decreased from 98.9 μg/m³ in 2013 to 64.9 μg/m³ in 2017. Therefore, China has achieved its Air Pollution Prevention and Control Plan goal of reducing the concentration of fine particulate matter in the BTH region by 25% by 2017. The PM_2.5 pollution in BTH plain areas showed a more significant change than mountains areas, with the highest PM_2.5 mass concentration in winter and the lowest in summer. The results of spatial autocorrelation and cluster analyses showed that the PM_2.5 mass concentration in the BTH region from 2013–2018 showed a significant spatial agglomeration, and that spatial distribution characteristics were high in the south and low in the north. Changes in PM_2.5 mass concentration in the BTH region were affected by both socio-economic factors and meteorological factors. Our results can provide a point of reference for making PM_2.5 pollution control decisions.

Comparative Analysis of Long-Term Variation Characteristics of SO2, NO2, and O3 in the Ecological and Economic Zones of the Western Sichuan Plateau, Southwest China

Sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and ozone (O₃) are important atmospheric pollutants that affect air quality. The long-term variations of SO₂ and NO₂ in 2008–2018 and O₃ in 2015–2018 in the relatively less populated ecological and economic zones of Western Sichuan Plateau, Southwest China were analyzed. In 2008–2018, the variations in SO₂ and NO₂ in the ecological zone were not significant, but Ganzi showed a slight upward trend. SO₂ decreased significantly in the economic zone, especially in Panzhihua, where NO₂ changes were not obvious. From 2015 to 2018, the concentration of O₃ in the ecological zone increased significantly, while the economic zone showed a downward trend. The rising trend of the concentration ratio of SO₂ to NO₂ in the ecological zone and the declining trend in the economic zone indicate that the energy consumption structure of these two zones is quite different. The lower correlation coefficients between NO₂ and O₃ in the Western Sichuan Plateau imply that the variations of O₃ are mainly affected by the regional background. The effects of meteorological factors on SO₂, NO₂, and O₃ were more obvious in the economic zone where there are high anthropometric emissions.

Spatiotemporal Trend Analysis of PM2.5 Concentration in China, 1999–2016

China is experiencing severe PM 2 . 5 (fine particles with a diameter of 2.5 μ g or smaller) pollution problem. Little is known, however, about how the increasing concentration trend is spatially distributed, nor whether there are some areas that experience a stable or decreasing concentration trend. Managers and policymakers require such information to make strategic decisions and monitor progress towards management objectives. Here, we present a pixel-based linear trend analysis of annual PM 2 . 5 concentration variation in China during the period 1999–2016, and our results provide guidance about where to prioritize management efforts and affirm the importance of controlling coal energy consumption. We show that 87.9% of the whole China area had an increasing trend. The drastic increasing trends of PM 2 . 5 concentration during the last 18 years in the Beijing–Tianjin–Hebei region, Shandong province, and the Three Northeastern Provinces are discussed. Furthermore, by exploring regional PM 2 . 5 pollution, we find that Tarim Basin endures a high PM 2 . 5 concentration, and this should have some relationship with oil exploration. The relationship between PM 2 . 5 pollution and energy consumption is also discussed. Not only energy structure reconstruction should be repeatedly emphasized, the amount of coal burned should be strictly controlled.

Health Risk and Resilience Assessment with Respect to the Main Air Pollutants in Sichuan

Rapid urbanization and industrialization in developing countries have caused an increase in air pollutant concentrations, and this has attracted public concern due to the resulting harmful effects to health. Here we present, through the spatial-temporal characteristics of six criteria air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃) in Sichuan, a human health risk assessment framework conducted to evaluate the health risk of different age groups caused by ambient air pollutants. Public health resilience was evaluated with respect to the risk resulting from ambient air pollutants, and a spatial inequality analysis between the risk caused by ambient air pollutants and hospital density in Sichuan was performed based on the Lorenz curve and Gini coefficient. The results indicated that high concentrations of PM_2.5 (47.7 μg m⁻³) and PM₁₀ (75.9 μg m⁻³) were observed in the Sichuan Basin; these two air pollutants posed a high risk to infants. The high risk caused by PM_2.5 was mainly distributed in Sichuan Basin (1.14) and that caused by PM₁₀ was principally distributed in Zigong (1.01). Additionally, the infants in Aba and Ganzi had high health resilience to the risk caused by PM_2.5 (3.89 and 4.79, respectively) and PM₁₀ (3.28 and 2.77, respectively), which was explained by the low risk in these two regions. These regions and Sichuan had severe spatial inequality between the infant hazard quotient caused by PM_2.5 (G = 0.518, G = 0.493, and G = 0.456, respectively) and hospital density. This spatial inequality was also caused by PM₁₀ (G = 0.525, G = 0.526, and G = 0.466, respectively), which is mainly attributed to the imbalance between hospital distribution and risk caused by PM_2.5 (PM₁₀) in these two areas. Such research could provide a basis for the formulation of medical construction and future air pollution control measures in Sichuan.

WRF-Chem modeling of particulate matter in the Yangtze River Delta region: Source apportionment and its sensitivity to emission changes

China has been troubled by high concentrations of fine particulate matter (PM_2.5) for many years. Up to now, the pollutant sources are not yet fully understood and the control approach still remains highly uncertain. In this study, four month-long (January, April, July and October in 2015) WRF-Chem simulations with different sensitivity experiments were conducted in the Yangtze River Delta (YRD) region of eastern China. The simulated results were compared with abundant meteorological and air quality observations at 138 stations in 26 YRD cities. Our model well captured magnitudes and variations of the observed PM_2.5, with the normal mean biases (NMB) less than ±20% for 19 out of the 26 YRD cities. A series of sensitivity simulations were conducted to quantify the contributions from individual source sectors and from different regions to the PM_2.5 in the YRD region. The calculated results show that YRD local source contributed 64% of the regional PM_2.5 concentration, while outside transport contributed the rest 36%. Among the local sources, industry activity was the most significant sector in spring (25%), summer (36%) and fall (33%), while residential source was more important in winter (38%). We further conducted scenario simulations to explore the potential impacts of varying degrees of emission controls on PM_2.5 reduction. The result demonstrated that regional cooperative control could effectively reduce the PM_2.5 level. The proportionate emission controls of 10%, 20%, 30%, 40% and 50% could reduce the regional mean PM_2.5 concentrations by 10%, 19%, 28%, 37% and 46%, respectively, and for places with higher ambient concentrations, the mitigation efficiency was more significant. Our study on source apportionment and emission controls can provide useful information on further mitigation actions.

Case study of spring haze in Beijing: Characteristics, formation processes, secondary transition, and regional transportation

Continuous haze monitoring was conducted from 12:00 3 April to 12:00 8 April 2016 in Beijing, China to develop a more detailed understanding of spring haze characteristics. The PM_2.5 concentration ranged from 6.30 to 165 μg m^-3 with an average of 63.8 μg m^-3. Nitrate was the most abundant species, accounting for 36.4% of PM_2.5, followed by organic carbon (21.5%), NH₄⁺ (19.3%), SO₄^2- (18.8%), and elemental carbon (4.10%), indicating the key role of nitrate in this haze event. Species contribution varied based on the phase of the haze event. For example, sulfate concentration was high during the haze formation phase, nitrate was high during the haze, and secondary organic carbon (SOC) had the highest contribution during the scavenging phase. The secondary transition of sulfate was influenced by SO₂, followed by relative humidity (RH) and O_x (O₃+NO₂). Nitrate formation occurred in two stages: through NO₂ oxidation, which was vulnerable to O_x; and by the partitioning of N (+5) which was susceptible to RH and temperature. SOC tended to form when O_x and RH were balanced. According to hourly species behavior, sulfate and nitrate were enriched during haze formation when the mixed layer height decreased. However, SOC accumulated prior to the haze event and during formation, which demonstrated the strong contribution of secondary inorganic aerosols, and the limiting contribution of SOC to this haze case. Investigating backward trajectories showed that high speed northwestern air masses following a straight path corresponded to the clear periods, while southwesterly air masses which traversed heavily polluted regions brought abundant pollutants to Beijing and stimulated the occurrence of haze pollution. Results indicate that the control of NO₂ needs to be addressed to reduce spring haze. Finally, the correlation between air mass trajectories and pollution conditions in Beijing reinforce the necessity of inter-regional cooperation and control.

Comparison of Ground-Based PM2.5 and PM10 Concentrations in China, India, and the U.S

Urbanization and industrialization have spurred air pollution, making it a global problem. An understanding of the spatiotemporal characteristics of PM_2.5 and PM₁₀ concentrations (particulate matter with an aerodynamic diameter of less than 2.5 μm and 10 μm, respectively) is necessary to mitigate air pollution. We compared the characteristics of PM_2.5 and PM₁₀ concentrations and their trends of China, India, and the U.S. from 2014 to 2017. Particulate matter levels were lowest in the U.S., while China showed higher concentrations, and India showed the highest. Interestingly, significant declines in PM_2.5 and PM₁₀ concentrations were found in some of the most polluted regions in China as well as the U.S. No comparable decline was observed in India. A strong seasonal trend was observed in China and India, with the highest values occurring in winter and the lowest in summer. The opposite trend was noted for the U.S. PM_2.5 was highly correlated with PM₁₀ for both China and India, but the correlation was poor for the U.S. With regard to reducing particulate matter pollutant concentrations, developing countries can learn from the experiences of developed nations and benefit by establishing and implementing joint regional air pollution control programs.

Spatial patterns and temporal variations of six criteria air pollutants during 2015 to 2017 in the city clusters of Sichuan Basin, China

Spatiotemporal variations of six criteria air pollutants and influencing factors in the city clusters of Sichuan Basin were studied based on real-time hourly concentrations of PM_2.5 (the particles with diameters smaller than 2.5μm), PM₁₀ (the particles with diameters smaller than 10μm), SO₂, NO₂, CO and O₃ and routine meteorological data during the years from 2015 to 2017. The Sichuan Basin was further categorized into four regions: West, south, northeast Sichuan Basin (WSB, SSB and NESB) and plateau of west Sichuan Basin (PWSB) to better understand regional air pollution characteristics. Heavy air pollution was mainly induced by high PM_2.5 or ozone concentrations in the cities clusters of Sichuan Basin. The compound air pollution characteristics existed in WSB with simultaneously high concentrations of PM_2.5 and ozone, while PM_2.5 concentrations in SSB were the highest among the four regions and especially in the city of Zigong with maximum PM_2.5 concentration of 109.3μgm^-3 in winter. The MDA8 (daily maximum 8-hour average surface O₃ concentrations) more frequently exceeded CAAQS (Chinese Ambient Air Quality Standards) Grade I and II standards in Ziyang, Guang'an and Liangshan than the other cities maybe due to joint effects of industry emissions and regional transportation from surrounding cities. Annual (diurnal) variations of the pollutants with the exception of ozone showed "U" (flat "W") shape, while the ozone exhibited the opposite trends inside Sichuan Basin (WSB, SSB and NESB). Ozone pollution was more dependent on vehicle emissions inside Sichuan Basin, and industry had more important effects on ozone in the cities of PWSB with less vehicles. Severe ozone pollution can be formed easily under the weather conditions of high temperature, long sunshine duration and low RH (relative humidity) inside Sichuan Basin. High ozone concentrations in winter in PWSB may be partly transported from the other surrounding cities.