Temporal and Spatial Variation in, and Population Exposure to, Summertime Ground-Level Ozone in Beijing

High summer background O3 levels in the desert of northwest China

Generally speaking, the precursors of ozone (O3), nitrogen oxides and volatile organic compounds are very low in desert areas due to the lack of anthropogenic emissions and natural emissions, and thus O3 concentrations are relatively low. However, high summer background concentrations of about 100 µg/m3 or 60 ppb were found in the Alxa Desert in the highland of northwest China based on continuous summer observations from 2019 to 2021, which was higher than the most of natural background areas or clean areas in world for summer O3 background concentrations. The high O3 background concentrations were related to surface features and altitude. Heavy-intensity anthropogenic activity areas in desert areas can cause increased O3 concentrations or pollution, but also generated O3 depleting substances such as nitrous oxide, which eventually reduced the regional O3 baseline values. Nitrogen dioxide (NO2) also had a dual effect on O3 generation, showing promotion at low concentrations and inhibition at high concentrations. In addition, sand-dust weather reduced O3 clearly, but O3 eventually stabilized around the background concentration values and did not vary with sand-dust particulate matter.

Observational study of ground-level ozone and climatic factors in Craiova, Romania, based on one-year high-resolution data

Air pollution is a multifaceted issue affecting people's health, environment, and biodiversity. Gaining comprehension of the interactions between natural and anthropocentric pollutant concentrations and local climate is challenging. This study aims to address the following two questions: (1) What is the influential mechanism of climatic and anthropogenic factors on the ground-level ozone (O3) concentrations in an urban environment during different seasons? (2) Can the ozone weekend effect be observed in a medium-sized city like Craiova, and under which conditions? In order to answer these questions, ozone interactions with meteorological parameters (temperature, pressure, relative humidity) and pollutant concentrations (particulate matter, carbon dioxide, volatile organic compounds, formaldehyde, nitrogen dioxide, nitric oxide and carbon monoxide) is evaluated based on a one-year dataset given by a low-cost sensor and one-year dataset provided by the National Environment Agency. Using two statistical analysis programs, Python and SPSS, a good understanding of the correlations between these variables and ozone concentration is obtained. The SPSS analysis underscores the significant impact of three meteorological factors and nine other pollutants on the ozone level. A positive correlation is noticed in the summer when sunlight is intense and photochemical reactions are elevated. The relationship between temperature and ozone concentration is strong and positive, as confirmed by Spearman's rho correlation coefficient (r = 0.880). A significant negative correlation is found between relative humidity and ozone (r = -0.590). Moreover, the analysis shows that particulate matter concentrations exhibit a significant negative correlation with ozone (r ≈ -0.542), indicating that higher particulate matter concentrations reduce ozone levels. Volatile organic compounds show a significant negative correlation with ozone (r = -0.156). A negative relationship between ozone and carbon dioxide (r = -0.343), indicates that elevated carbon dioxide levels might also suppress ozone concentrations. A significant positive correlation between nitrogen dioxide and ozone (r = 0.060), highlights the role of nitrogen dioxide in the production of ozone through photochemical reactions. However, nitric oxide shows a negative correlation with ozone (r = -0.055) due to its role in ozone formation. Carbon monoxide has no statistically significant effect on ozone concentration. To observe the differences between weekdays and weekends, T-Test was used. Even though significant differences were observed in temperature, humidity, carbon dioxide, volatile organic compounds, nitrogen dioxide, nitric oxide and carbon monoxide levels between weekdays and weekends, the T-Test did not highlight a significant weekend ozone effect in a mid-sized city as Craiova. Using Python, the daily values were calculated and compared with the limit values recommended by the World Health Organization (WHO) and European Environment Agency (EEA). The WHO O3 recommended levels were exceeded for 13 times in one year. This study offers a comprehensive understanding of ozone pollution in a mid-sized city as Craiova, serving as a valuable reference for local decision-makers. It provides critical insights into the seasonal dynamics of ozone levels, emphasizing the significant role of temperature in ozone formation and the complex interactions between various pollutants and meteorological factors.

Quantifying ecological and health risks of ground-level O3 across China during the implementation of the "Three-year Action Plan for Cleaner Air"

After China implemented the Air Pollution Prevention and Control Action Plan (APPCAP), PM_2.5 concentrations decreased but were still higher than national standards in major areas and ozone (O₃) concentration increased unintentionally. To further decrease PM_2.5 concentrations and reduce days with severe air pollution, the government promulgated the "Three-year (2018-2020) Action Plan for Cleaner Air" (the Three-year Action Plan) in 2018. During the three-year Action Plan, a few studies reported a continuous decline in PM_2.5, but it is unclear whether O₃ and its effects also increase with the decrease of PM_2.5 like during APPCAP. In this study, for the first time, we systematically assessed changes in ground-level O₃ concentrations and related ecological and health risks during the period of the Three-year Action Plan using nationwide O₃ measurements. The national MDA8, Exceedance, and SOMO35 indicators were reduced by 3.8%, 28.5%, and 12.6%, respectively, ecological risk indicators of M12, M7, SUM06, AOT40, and W126 were reduced by 5.4%, 5.6%, 19.5%, 15.4%, and 18.6%, respectively, from 2018 to 2020. Spatially, the greatest reduction in all the indicators except MDA8 occurred in Pearl River Delta, followed by Fen Wei Plains, while Beijing-Tianjin-Hebei, Chengdu-Chongqing, and Yangtze River Delta presented relatively small reductions. Between 2018 and 2020, the production losses caused by O₃ for wheat and rice decreased by 21.4% and 17.6%, respectively. Long-term exposure to O₃ across China over 2020 was estimated to cause about 160,795 (95% CI: 81,515-312,983) for all-cause mortality, 107,128 (95% CI: 36,703-173,823) for cardiovascular mortality, and 34,444 (95% CI: 0-72,609) for respiratory mortality, indicating decreases of 9.93%, 9.86%, and 9.78%, respectively, compared to the year 2018. Taken together, our results provided the first direct evidence for China's efforts to control O₃ pollution in recent years.

New Insights for Tracking Global and Local Trends in Exposure to Air Pollutants

Over six million people die prematurely each year from exposure to air pollution. Current air quality metrics insufficiently monitor exposure to air pollutants. This gap hinders the ability of decisionmakers to address the public health impacts of air pollution. To spur new emissions control policies and ensure implemented solutions realize meaningful gains in environmental health, we develop a framework of public-health-focused air quality indicators that quantifies over 200 countries' trends in exposure to particulate matter, ozone, nitrogen oxides, sulfur dioxide, carbon monoxide, and volatile organic compounds. We couple population density to ground-level pollutant concentrations to derive population-weighted exposure metrics that quantify the pollutant levels experienced by the average resident in each country. Our analyses demonstrate that most residents in 171 countries experience pollutant levels exceeding international health guidelines. In addition, we find a negative correlation between temporal trends in ozone and nitrogen oxide concentrations, which─when qualitatively interpreted with a simple atmospheric chemistry box model─can help describe the apparent tradeoff between the mitigation of these two pollutants on local scales. These novel indicators and their applications enable regulators to identify their most critical pollutant exposure trends and allow countries to track the performance of their emission control policies over time.

A machine learning model to estimate ground-level ozone concentrations in California using TROPOMI data and high-resolution meteorology

Estimating ground-level ozone concentrations is crucial to study the adverse health effects of ozone exposure and better understand the impacts of ground-level ozone on biodiversity and vegetation. However, few studies have attempted to use satellite retrieved ozone as an indicator given their low sensitivity in the boundary layer. Using the Troposphere Monitoring Instrument (TROPOMI)'s total ozone column together with the ozone profile information retrieved by the Ozone Monitoring Instrument (OMI), as TROPOMI ozone profile product has not been released, we developed a machine learning model to estimate daily maximum 8-hour average ground-level ozone concentration at 10 km spatial resolution in California. In addition to satellite parameters, we included meteorological fields from the High-Resolution Rapid Refresh (HRRR) system at 3 km resolution and land-use information as predictors. Our model achieved an overall 10-fold cross-validation (CV) R² of 0.84 with root mean square error (RMSE) of 0.0059 ppm, indicating a good agreement between model predictions and observations. Model predictions showed that the suburb of Los Angeles Metropolitan area had the highest ozone levels, while the Bay Area and the Pacific coast had the lowest. High ozone levels are also seen in Southern California and along the east side of the Central Valley. TROPOMI data improved the estimate of extreme values when compared to a similar model without it. Our study demonstrates the feasibility and value of using TROPOMI data in the spatiotemporal characterization of ground-level ozone concentration.

When a Generalized Linear Model Meets Bayesian Maximum Entropy: A Novel Spatiotemporal Ground-Level Ozone Concentration Retrieval Method

In China, ground-level ozone has shown an increasing trend and has become a serious ambient pollutant. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) is urgently needed. Generalized linear models (GLMs) and Bayesian maximum entropy (BME) models are practical for predicting GOCs. However, GLMs have limited capacity to capture temporal variations and can miss some short-term and regional patterns, while the performance of BME models may degrade in cases of sparse or imperfect monitoring networks. Thus, to predict nationwide 1 km monthly average GOCs for China, we designed a novel hybrid model containing three modules. (1) A GLM was established to accurately describe the variability in GOCs in the space domain. (2) A BME model incorporating GLM residuals was employed to capture the temporal variability of GOCs in detail. (3) A combination of GLM and BME models was developed based on the specific broad range of each submodel. According to the cross-validation results, the hybrid model exhibited superior performance, with coefficient of determination (R2) values of 0.67. The predictive performance of the large-scale and high-resolution hybrid model is superior to that in previous studies. The nationwide spatiotemporal variability of the GOCs derived from the hybrid model shows that they are valuable indicators for ground-level ozone pollution control and prevention in China.

Coordinated control of PM2.5 and O3 is urgently needed in China after implementation of the "Air pollution prevention and control action plan"

To improve air quality, China formulated the Air Pollution Prevention and Control Action Plan (APPCAP) in 2013. In the present study, the changes in the concentration of air pollutants after the implementation of APPCAP were investigated based on nationwide monitoring data. From the results, it is evident that the annual mean concentrations of PM_2.5, PM₁₀, SO₂, and CO show a significant downward trend over 2015-2018, with decreasing rates of 3.4, 4.1, 3.8, and 70 μg m^-3/year, respectively. However, no significant change was found in NO₂ while maximum daily 8 h average O₃ concentration (MDA8 O₃) was increased by 3.4 μg m^-3/year during the four years. Spatially, the highest decrease in PM_2.5 was found in Beijing-Tianjin-Hebei (BTH), followed by central China and northeast China, while the Pearl River Delta (PRD), Yungui Plateau, and northwest China showed less decreases. MDA8 O₃ had a higher increase in BTH, central China, Yangtze River Delta (YRD), and PRD. With the decrease in PM_2.5 in recent years, cumulative population exposure to PM_2.5 gradually decreased, whereas there was still more than 65% of the population exposing to annual PM_2.5 higher than the standard of 35 μg m^-3 in 2018. In contrast, the health effects of O₃ gradually increased with 13.1%, 14.3%, 20.4%, and 21.7% of the population exposed to unhealthy O₃ levels in summer from 2015 to 2018. O₃ pollution is causing severe health risks with estimated nationwide mortality of 70,024 (95% CI: 55,510-84,501), 79,159 (95% CI: 62,750-95,525), 105,150 (95% CI: 83,378-126,852), and 104,404 (95% CI: 82,784-125,956) in the four years, respectively. This clearly shows that the target of air pollution control in China shifts and coordinated control of PM_2.5 and O₃ is urgently needed after the successful implementation of APPCAP.

Evaluating the effectiveness of multiple emission control measures on reducing volatile organic compounds in ambient air based on observational data: A case study during the 2010 Guangzhou Asian Games

Volatile organic compounds (VOCs) play a crucial role in modulating air pollution by ozone and fine particles, particularly in urban areas. While in recent years short-term intervention actions for better air quality during big events in China did present good opportunities to examine the effectiveness of control measures in reducing anthropogenic VOCs emission, it is highly challenging to interpret the real effect of a specific control measure based on field monitoring data when a cocktail of control measures were adopted. Here we took the air quality intervention actions during the 16th Asian Games (AG) in Guangzhou as a case study to explore the impact of short-term multiple measures on VOCs reduction. The average mass concentrations of VOCs decreased by 52-68% during the AG. These percentages could not reflect emission reduction rates as the concentration might be also heavily impacted by dispersion conditions. Diagnostic ratios, such as methyl tert-butyl ether to carbon monoxide (MTBE/CO) and i-pentane/CO, decreased by over 60% during the AG, suggesting a substantial reduction in gasoline related emissions. A method linking emission reduction rates of two sources with their contribution percentages before and during the AG by using a receptor model was further formulated. With the available reduction rate of 34% for vehicular exhaust obtained during the traffic restriction drill in our previous study, VOCs emissions from gasoline evaporation and solvent use reduced by 45.7% and 13.6% during the AG, respectively. Total VOCs emissions decreased by 25.3% on average during the AG, and the emission control of vehicular exhaust, oil evaporation, and solvent use accounted for 17.0%, 6.3% and 2.0% of total VOCs emission reduction, respectively. This study presented an observed-based method with diagnostic/quantitative approaches to single out the effectiveness of each control measures in reducing VOCs emissions.

Yield and economic losses in maize caused by ambient ozone in the North China Plain (2014-2017)

Maize is the second most important crop per harvested area in the world. The North China Plain (NCP) is a highly populated and relevant agricultural region in China, experiencing some of the highest ozone (O₃) concentrations worldwide. It produces ~24% of the total maize production of China in years 2014-2017. For these years, we used observational O₃ data in combination with geostatistic methods to estimate county-level production and economic losses due to O₃ in the NCP. AOT40 (accumulated ozone exposure over an hourly threshold of 40 ppb) values during the maize growing season (90 days before maturity) progressively increased in the four consecutive years: 13.7 ppm h, 15.4 ppm h, 16.9 ppm h and 22.7 ppm h. Mean relative yield losses were 8.2% in 2014, 9.2% in 2015, 10.4% in 2016 and 13.4% in 2017. These yield losses, derived from exposure-response functions, resulted in crop production losses of 530.3 × 10⁴ t, 617.8 × 10⁴ t, 713.8 × 10⁴ t, and 953.4 × 10⁴ t, as well as economic losses of 2343 million USD, 2672 million USD, 1887 million USD, and 2404 million USD from 2014 to 2017. The NCP is a key area in China for monitoring the effectiveness of the clean air action policies aiming at reducing emissions of air pollutants. Despite these measures, O₃ concentrations have increased in NCP, and reduction of this pollutant are challenging. We suggest an increase in the number of rural air quality stations for better characterizing O₃ trends in cropland areas, as well as the application of different mitigation measures. They may involve more stringent air quality regulations and changes in crops, breeding tolerant cultivars and a crop management taking into account O₃ pollution.

Assessment of O3-induced yield and economic losses for wheat in the North China Plain from 2014 to 2017, China

Tropospheric ozone (O₃) is a pollutant of widespread concern in the world and especially in China for its negative effects on agricultural crops. For the first time, yield and economic losses of wheat between 2014 and 2017 were estimated for the North China Plain (NCP) using observational hourly O₃ data from 312 monitoring stations and exposure-response functions based on AOT40 index (accumulated hourly O₃ concentration above 40 ppb) from a Chinese study. AOT40 values from 2014 to 2017 during the wheat growing seasons (75-days, 44 before and 30 after mid-anthesis) ranged from 3.1 to 14.9 ppm h, 4.9-17.5 ppm h, 7.3-17.6 ppm h, and 0.5-18.6 ppm h, respectively. The highest AOT40 values were observed in the Beijing-Tianjin-Hebei region. The values of relative yield losses from 2014 to 2017 were in the ranges of 6.4-30.5%, 10.0-35.8%, 14.9-34.1%, and 21.6-38.2%, respectively. The total wheat production losses in NCP for 2014-2017 accounted for 18.5%, 22.7%, 26.2% and 30.8% in the whole production, while the economic losses amounted to 6,292 million USD, 8,524 million USD, 10,068 million USD, and 12,404 million USD, respectively. The important impact of O₃ in this area, which is of global importance, should be considered when assessing wheat yield production. Our results also show an increasing trend in AOT40, relative yield loss, total crop production loss and economic loss in the four consecutive years.

Evaluating the effects of surface O3 on three main food crops across China during 2015-2018

In order to tackle China's severe air pollution issue, the government has released the "Air Pollution Prevention and Control Action Plan" (known simply as the "Action Plan") since 2013. A recent study reported a decreased trend in PM_2.5 concentrations over 2013-2017, but O₃ pollution has become more serious. However, the effects of surface O₃ on crops are unclear after the implementation of the "Action Plan". Here, we evaluated the potential negative effects of surface O₃ on three main food crops (winter wheat, maize and rice) across China during 2015-2018 using nationwide O₃ monitoring data and AOT40-yield response functions. Results suggested that mean O₃ concentration, AOT40 and relative yield loss in China showed an overall upward trend from 2015 to 2018. During winter wheat, maize, single rice, double-early rice, and double-late rice growing seasons, mean O₃ concentration in recent years ranged from 38.6 to 46.9 ppb, 40.2-43.9 ppb, 39.3-42.2 ppb, 33.8-40.0 ppb, and 35.9-39.1 ppb, respectively, and AOT40 mean values ranged from 8.5 to 14.3 ppm h, 10.5-13.4 ppm h, 9.8-11.9 ppm h, 5.2-9.2 ppm h, and 8.0-9.5 ppm h, respectively. O₃-induced yield reductions were estimated to range from 20.1 to 33.3% for winter wheat, 5.0-6.3% for maize, 7.3-8.8% for single rice, 3.9-6.8% for double-early rice and 5.9-7.1% for double-late rice. O₃-induced production losses for winter wheat, maize, single rice, double-early rice, and double-late rice totaled 39.5-88.2 million metric tons, 12.6-21.0 million metric tons, 9.5-11.3 million metric tons, 1.2-1.8 million metric tons, and 2.2-2.7 million metric tons, respectively, and the corresponding economic losses totaled 14.3-32.0 billion US$, 3.9-6.5 billion US$, 3.9-4.6 billion US$, 0.5-0.7 billion US$, and 0.9-1.1 billion US$, respectively. Our results suggested that the government should take effective measures to reduce O₃ pollution and its effects on agricultural production.

Using low-cost sensors to monitor indoor, outdoor, and personal ozone concentrations in Beijing, China

High concentrations of ground-level ozone (O₃) have been measured outdoors across China but there are limited measurements of O₃ in microenvironments, including in homes, and for personal exposure. This highlights the need for cheaper methods to accurately make these measurements and to better capture fine-scale spatial variability in O₃ across cities. With this in mind, we conducted a pilot study at six homes in Beijing, China, over 12 days to evaluate the use of portable, low-cost, time-resolved monitors for measuring O₃ indoors and outdoors. We also assessed personal exposure for one adult in each home for two 48 hour periods using backpack-mounted monitors. Prior to and following sampling we collocated all monitors with a reference analyzer; we used data from these colocations to generate linear calibrations which we applied to all monitor data. Calibration slopes did not change significantly over the study although some intercepts differed. The average limit of detection (LOD) was 7.0 ppb, average root mean square error was 16.7 ppb, mean absolute error was 13.3 ppb and normalized root mean square error was 33%. Performance varied substantially between sensors, underscoring the importance of monitor-specific calibrations and determinations of measurement error. Outdoor concentrations varied spatially, with home-specific peak hourly averages of 32-165 ppb; indoor concentrations ranged from below the LOD to 15 ppb. Hour-averaged personal exposure was generally higher than O₃ indoors, and at times exceeded ambient O₃ indicating contributions to personal exposure from ambient sources of O₃ away from the home. This work illustrates the feasibility of using these monitors to characterize distributions of O₃ spatially and temporally when differences in concentrations are large, and outlines considerations for using these monitors to measure personal exposure.

Economic losses due to ozone impacts on human health, forest productivity and crop yield across China

China's economic growth has significantly increased emissions of tropospheric ozone (O₃) precursors, resulting in increased regional O₃ pollution. We analyzed data from >1400 monitoring stations and estimated the exposure of population and vegetation (crops and forests) to O₃ pollution across China in 2015. Based on WHO metrics for human health protection, the current O₃ level leads to +0.9% premature mortality (59,844 additional cases a year) with 96% of populated areas showing O₃-induced premature death. For vegetation, O₃ reduces annual forest tree biomass growth by 11-13% and yield of rice and wheat by 8% and 6%, respectively, relative to conditions below the respective AOT40 critical levels (CL). These CLs are exceeded over 98%, 75% and 83% of the areas of forests, rice and wheat, respectively. Using O₃ exposure-response functions, we evaluated the costs of O₃-induced losses in rice (7.5 billion US$), wheat (11.1 billion US$) and forest production (52.2 billion US$) and SOMO35-based morbidity for respiratory diseases (690.9 billion US$) and non-accidental mortality (7.5 billion US$), i.e. a total O₃-related cost representing 7% of the China Gross Domestic Product in 2015.

Characteristics of Ozone Pollution, Regional Distribution and Causes during 2014–2018 in Shandong Province, East China

The summer ozone pollution of Shandong province has become a severe problem in the period 2014–2018. Affected by the monsoon climate, the monthly average ozone concentrations in most areas were unimodal, with peaks in June, whereas in coastal areas the concentrations were bimodal, with the highest peak in May and the second highest peak in September. Using the empirical orthogonal function method, three main spatial distribution patterns were found. The most important pattern proved the influences of solar radiation, temperature, and industrial structure on ozone. Spatial clustering analysis of the ozone concentration showed Shandong divided into five units, including Peninsula Coastal area (PC), Lunan inland area (LN), Western Bohai area (WB), Luxi plain area (LX), and Luzhong mountain area (LZ). Influenced by air temperature and local circulation, coastal cities had lower daytime and higher nighttime ozone concentrations than inland. Correlation analysis suggested that ozone concentrations were significantly positively correlated with solar radiation. The VOCs from industries or other sources (e.g., traffic emission, petroleum processing, and chemical industries) had high positive correlations with ozone concentrations, whereas NOx emissions had significantly negatively correlation. This study provides a comprehensive understanding of ozone pollution and theoretical reference for regional management of ozone pollution in Shandong province.

Spatiotemporal Distribution of PM2.5 and O3 and Their Interaction During the Summer and Winter Seasons in Beijing, China

This study analyzed the spatiotemporal variations in PM2.5 and O3, and explored their interaction in the summer and winter seasons in Beijing. To this aim, hourly PM2.5 and O3 data for 35 air quality monitoring sites were analyzed during the summer and winter of 2016. Results suggested that the highest PM2.5 concentration and the lowest O3 concentration were observed at traffic monitoring sites during the two seasons. A statistically significant (p < 0.05) different diurnal variation of PM2.5 was observed between the summer and winter seasons, with higher concentrations during daytime summer and nighttime winter. Diurnal variations of O3 concentrations during the two seasons showed a single peak, occurring at 16:00 and 15:00 in summer and winter, respectively. PM2.5 presented a spatial pattern with higher concentrations in southern Beijing than in northern areas, particularly evident during wintertime. On the contrary, O3 concentrations presented a decreasing spatial trend from the north to the south, particularly evident during summer. In addition, we found that PM2.5 concentrations were positively correlated (p < 0.01, r = 0.57) with O3 concentrations in summer, but negatively correlated (p < 0.01, r = −0.72) with O3 concentrations in winter.