Characterization of Spatial and Temporal Variations in Air Pollutants and Identification of Health Risks in Xi’an, a Heavily Polluted City in China

Abstract

The study of the temporal and spatial characteristics of air pollutants in heavily polluted cities is extremely important for analyzing the causes of pollution and achieving a viable means of control. Such characteristics in the case of Xi’an, a typical heavily polluted city in Fenwei Plain, China, have remained unclear due to limitations in data accuracy and research methods. The monthly, daily, and hourly patterns of O₃ and particulate matter (PM_2.5 and PM₁₀) are analyzed in this study using on-site data provided by an urban air quality monitoring network. The analysis of variance (ANOVA) method was used to compare differences in pollutant concentrations during different seasons and time periods. The spatial distributions of O₃, PM_2.5, and PM₁₀ at different time points following interpolation of the air quality monitoring sites have been analyzed. The results show that the O₃ concentration from 12 p.m. to 3 p.m. was significantly higher than that in the morning and evening, and the concentrations of PM_2.5 and PM₁₀ from 7 p.m. to 10 p.m. were significantly higher than those in the morning and afternoon. The number of qualified days for PM_2.5 was less than 30 and unqualified days for O₃ was more than 100 in 2019. There is a potential risk of exposure to pollution with associated health risks. Even on the same day, the spatial pollutant distributions at different time points can differ significantly. This study provides a scientific basis for reducing O₃ and particulate matter exposure. Outdoor activities in the morning in summer are more beneficial to reduce O₃ exposure, and outdoor activities should be curtailed in the evening in winter to reduce particulate exposure. This study provides a scientific basis for the government to formulate public health policies to reduce pollution exposure from outdoor activities.

1. Introduction

Air pollution is caused by human activities and natural processes that discharge certain substances into the atmosphere, endangering human health and welfare when reaching a sufficient concentration that is time dependent [1]. Anthropogenic emissions originate mainly from industrial production and transportation [2,3]. Common air pollutants in cities include particulate matter (PM₁₀), fine particulate matter (PM_2.5), and ozone (O₃). Various pollutants have a concomitant impact on urban spaces [4,5]. The superposition of different health hazards caused by different types of pollution has led to an increasing incidence rate and mortality [6]. These hazards include damage to the human respiratory and cardiopulmonary systems caused by O₃, low fertility, respiratory diseases, and arteriosclerosis caused by PM_2.5 [7,8,9,10,11]. The key to preventing and controlling pollution requires a full understanding of the spatial and temporal characteristics of air pollutants to effectively reduce public exposure to pollution and improve the quality of public life.

Xi’an is located in the western part of the fault basin of the Fenwei Plain, which has been identified as a key area for national air pollution prevention and control by the Ministry of Ecology and Environment of the People’s Republic of China [12]. As the only provincial capital city in the Fenwei Plain, Xi’an has the largest population affected by severe pollution. Coal and coking industries are clustered in the Fenwei Plain, which is surrounded by mountains on all sides. An anti-cyclonic airflow stagnation area is readily formed in the plain area because of the blocking effect of the mountains and the sinking of a leeward slope airflow. Due to the significant impact of ground aggregation, pollutants do not easily disperse after aggregation. The combined effect of pollutant emissions from cities in the plain is obvious [13,14]. The frequent hazy conditions in winter affect the mental health of the inhabitants, and the population is exposed to unbearable air conditions [15]. Pollutants pose a threat to public health, leading to increasing medical expenses, which seriously impact the economic development of the region [16,17]. As Xi’an has the largest population and the most developed economy in Fenwei Plain, an analysis of the spatial and temporal pollutant characteristics can inform the prevention and control of pollutants in other cities.

This study assumes significant differences in pollutant concentrations in time and space. The objective of this study is to analyze the monthly, daily, and hourly patterns of O₃ and particulate matter (PM_2.5 and PM₁₀) and to investigate the temporal differences and spatial distribution characteristics of pollutant concentrations. Five stations were first selected to study the monthly and daily concentration changing characteristics of O₃, PM_2.5, and PM₁₀ in Xi’an in 2019. The hourly characteristics of O₃, PM_2.5, and PM₁₀ in five typical polluted days were analyzed using the trend change analysis method, and the temporal characteristics of each pollutant were studied. The analysis of variance (ANOVA) method was used to compare differences in pollutant concentrations in different seasons and time periods. Two time points, 7:00 a.m. and 3:00 p.m., were selected in the analysis of five typical O₃ polluted days (3–7 August 2018) along with three time points, 7:00 a.m., 12:00 p.m., and 6:00 p.m., for five typical PM polluted days (2–6 January 2019). The inverse distance weighting (IDW) method was used to interpolate the pollutant data from 209 stations to obtain the spatial distribution of different pollutants at different time points in the central urban area. Changes in the spatial characteristics of air pollution in Xi’an were assessed using horizontal and vertical comparisons. This analysis provides a scientific basis for air pollution control and prevention in Xi’an, which will reduce public exposure to pollution and improve the environmental health quality of the residents. The structure of this study includes an introduction, methods, results and discussion, and conclusions.

2. Methods

2.1. Region Researched

Xi’an is an important city in the western region of China with a total area of 10,108 km². It has an east-to-west length of ca. 204 km and a north-to-south width of ca. 116 km. The total central urban area of Xi’an is 610.14 km², according to the central city scope delineated in Xi’an Overall Plan (2008–2020) [18]. The scope and location of the city’s territory and the central urban area are illustrated in Figure 1. Xi’an is located in the western part of the fault basin of the Fenwei Plain, which is one of the most air-polluted areas in the country. According to the national air quality for January to December 2018 reported by the Ministry of Ecology and Environment, Xi’an ranked 158th of 169 cities suffering serious air pollution [12]. In summer, the temperature in Xi’an is high, and radiation is strong, facilitating O₃ generation. Thus, the concentration of O₃ remains high. In Xi’an from 2014 to 2017, the number of unqualified days in terms of O₃ concentration in summer increased from 8 to 61 [19]. There is serious air pollution in Xi’an in both winter and summer.

2.2. Source of the Data

The air quality data were provided by the Xi’an air quality automatic monitoring network. There are 209 national standard three-parameter air quality monitoring stations; the locations are shown in Figure 1. The three parameters are PM₁₀, PM_2.5, and O₃. The average concentrations of PM_2.5 and PM₁₀ are reported hourly and daily, while that of O₃ is reported hourly and daily (a maximum of 8 h). The PM_2.5 and PM₁₀ concentrations are measured using a laser scattering method with a range of 0.01–2000 μg/m³, a resolution of 0.01 μg/m³, and an indication error of ±10%. O₃ is measured using an electrochemical method with a range of 0–500 ppb and an indication error of ±10%. Stations used to analyze temporal changes in atmospheric pollutants are based on data completeness and represent different zones.

2.3. The Analysis of Variance (ANOVA) Method

ANOVA was used to compare the differences in the concentrations of O₃, PM_2.5, and PM₁₀ in different seasons (March–May in spring, June–August in summer, September–November in autumn, and December–February in winter) and different outdoor activity periods (6–9 a.m., 12–3 p.m., 7–10 p.m.). The formulas are provided below:

$$F=\frac{SSB/(k-1)}{SSE/(N-k)}$$

(1)

$$SSB={\sum }_{i=1}^k{n}_i{\left({\overline{X}}_i-\overline{X}\right)}^2$$

(2)

$$SSE={\sum }_{i=1}^k{\sum }_{j=1}^{n_i}{\left(X_{ij}-{\overline{X}}_i\right)}^2$$

(3)

where F is the statistic, SSB is the sum of squares between groups, SSE is the sum of squares of the residuals, k is the number of groups, N is the total sample number, ${\overline{X}}_i$ is the mean of the i-th group, n_i is the sample size of the i-th group, and X_ij is the j-th sample in the i-th group.

2.4. Inverse Distance Weighted (IDW) Interpolation Method

The spatial interpolation method was used to study the spatial and temporal changing characteristics of air pollutants in the interior space of the city based on the high-density stations of the urban air quality monitoring network. ArcGIS is a desktop application geographic information system platform that integrates many functions, such as spatial data display, editing, query retrieval, statistics, report generation, spatial analysis, and advanced mapping. Surface interpolation is an ArcGIS tool that creates continuous (or predicted) surfaces based on sampling points. Inverse distance weight interpolation (IDW) is a surface interpolation method that can determine the pixel value through a linear weight combination of a set of sampling points. The weight is an inverse distance function, and the assumption is that the mapped variable can be reduced due to the influence of the distance on the sampling position. The closer the interpolation point is to the known point, the greater the weight. The IDW method has been used in this study as it exhibits more advantages in retaining the original value of the monitoring stations, and the resultant interpolation results are smoother than those generated using other interpolation methods. The formulas are provided below:

$$Z=\frac{{\sum }_{i=1}^n\frac{1}{{\left(D_i\right)}^p}{Z}_i}{{\sum }_{i=1}^n\frac{1}{{\left(D_i\right)}^p}}$$

(4)

$$D_i=\sqrt{{\left(X-X_i\right)}^2+{\left(Y-Y_i\right)}^2}$$

(5)

where Z is the estimated value of the interpolation point. The coordinates of Z are set to (X,Y). The actual value of the i-th data point is set to Z_i, and its coordinates are (X_i,Y_i). D_i is the distance from the interpolation point to the i-th data point, and p is the power of the distance from the interpolation point to the i-th data point.

3. Results and Discussion

3.1. Daily Pollutant Patterns

A comparison of the ambient air quality standard GB-3095-2012 released by China in 2012 and the global air quality standard AQG2021 updated by the WHO in 2021 is provided in

Table 1. Comparison of pollutant index limits for China GB-3095-2012 and WHO AQG2021.

Pollutant	Index	GB-3095-2012		AQG2021
		Class 1	Class 2	Stage 1	Stage 2	Stage 3	Stage 4	AQG
PM2.5 (μg/m3)	Annual average	15	35	35	25	15	10	5↓
	24-h average	35	75	75	50	37.5	25	15↓
PM10 (μg/m3)	Annual average	40	70	70	50	30	20	15↓
	24-h average	50	150	150	100	75	50	45↓
O3 (μg/m3)	Peak season	-	-	100	70	-	-	60
	Daily maximum 8-h average	100	160	160	120	-	-	100
	1-h average	160	200	-	-	-	-	-

↓ Indicates a value below the standard. - Indicates none.

[20,21]. China’s ambient air functional areas are divided into two categories. The first category includes nature reserves, scenic spots, and other areas that require special protection. The second category encompasses residential areas, commercial and civil-military mixed areas, cultural areas, industrial areas, and rural areas. The Class 1 concentration limit values in Table 1 apply to the first category, while the Class 2 concentration limit values apply to the second category. The Class 2 concentration limit values in China are the same as the Stage 1 WHO target values.

In

Table 2. Number of unqualified/qualified days for the pollutants over 365 days in 2019.

Station	Unqualified Days for O3		Unqualified/Qualified Days for PM2.5		Unqualified/Qualified Days for PM10
	>160 (μg/m3)	>100 (μg/m3)	>75 (μg/m3)	≤15 (μg/m3)	>150 (μg/m3)	≤45 (μg/m3)
Station 1	4	126	64	27	32	108
Station 2	40	127	75	14	48	65
Station 3	30	117	85	9	65	66
Station 4	19	107	96	28	79	77
Station 5	48	104	69	28	67	56

, the average daily values of PM_2.5, PM₁₀, and O₃ (for a maximum of 8 h) for China and those generated by the WHO were used as criteria to determine the number of unqualified days for pollution at the 5 stations in Xi’an for 365 days in 2019. China uses the Class 2 concentration limit, while the WHO applies the final Air Quality Guideline (AQG) in Table 1. The air quality reported by the Daiwang Street Office (Station 1) and Xiliu Street Office (Station 4) in the suburbs is better than that reported by the Hansenzhai Street Office (Station 2), Taoyuan Street Office (Station 3), and Caotan Ecological Industrial Park (Station 5) in the central urban area, according to the Chinese standard. Applying the WHO standard, the number of qualified days for PM_2.5 at the 5 stations was less than 30, the number of unqualified days for O₃ was more than 100, and the number of qualified days for PM₁₀ was less than 100 with the exception of Station 1. These data suggest a potential risk of exposure to pollution with consequent health risks [7,8]. The daily variations in O₃, PM_2.5, and PM₁₀ concentrations are illustrated in Figure 2, Figure 3 and Figure 4. The black dotted lines in the figures demonstrate that the air quality in Xi’an is still far from the AQG of the WHO.

3.2. Hourly Pollutant Patterns and Time Period Differences

3.2.1. Hourly O₃ Concentrations and Time Period Differences

Data from the five air quality monitoring stations allowed a comparison of the 24-h O₃ concentrations on five consecutive typical polluted days (3–7 August 2018). The five stations selected in this instance are different from the five stations used to analyze the monthly pollutant patterns. We chose the monitoring stations on the basis of data integrity and a representation of different locations. The selected stations include Laodian Town (Station 6), Caotan Ecological Industrial Park (Station 7), Liyang Street Office (Station 8), Changle Middle Road Street Office (Station 9), and Xiaozhai Road Street Office (Station 10). It can be seen from the entries in

Table 3. Variation in the amplitude (daily maximum minus daily minimum) of the O₃ concentration (μg/m³) at 5 monitoring stations over 5 days.

Monitoring Station	3 August	4 August	5 August	6 August	7 August	Average Value for 5 Days
Station 6	220	194	215	193	201	205
Station 7	107	228	246	203	239	205
Station 8	62	132	233	109	103	127
Station 9	130	308	262	343	238	256
Station 10	138	279	259	325	240	248

that the amplitude (daily maximum minus daily minimum) of O₃ concentration over 24 h recorded by the five monitoring stations on the 5 consecutive days ranged from 62 to 343 μg/m³. The O₃ concentration reported by the same station on the same day varied widely within the 24-h period, as revealed in Figure 5. A low concentration level was a feature of the 0:00 to 8:00 a.m. period, with a discernible increase at 9:00 a.m., maintaining a high concentration level from 1:00 p.m. to 6:00 p.m. to reach the peak value. The concentration of O₃ decreased at later times following the peak.

The results of ANOVA were used to analyze the differences in O₃ concentrations during the three outdoor activity periods (6–9 a.m., 12–3 p.m., and 7–10 p.m.). The results showed that the O₃ concentration in the afternoon was significantly higher than that in the morning and evening (p < 0.001 ***), and the O₃ concentration in the evening was significantly higher than that in the morning (p < 0.001 ***), as revealed in Figure 6a. This response is closely related to changes in temperature, where a higher temperature enhances the rate of photochemical reactions generating more O₃ [22,23,24]. To reduce exposure to O₃, outdoor activities should be curtailed in summer afternoons, and outdoor activities should be conducted as late in the evening as possible.

3.2.2. Hourly PM_2.5, and PM₁₀ Concentrations and Time Period Differences

Five air quality monitoring stations were selected to compare the changes in the 24-h concentrations of PM_2.5 and PM₁₀ over five typical pollution days (2–6 January 2019). The selected stations include Headquarters Economic Park Station (Station 11), Chang’an Road Street Office (Station 12), Daming Palace National Heritage Park (Station 13), Xiyi Road Street Office (Station 14), and North High-speed Railway Station (Station 15). It can be seen from

Table 4. Variations in the amplitude (daily maximum minus daily minimum) of the PM_2.5 concentration (μg/m³) at 5 monitoring stations over 5 days.

Monitoring Station	2 January	3 January	4 January	5 January	6 January	Average Value for 5 Days
Station 11	102	108	101	132	90	109
Station 12	77	67	117	107	73	88
Station 13	92	61	59	96	78	77
Station 14	84	61	106	110	74	87
Station 15	107	98	149	81	107	108

and

Table 5. Variations in the amplitude (daily maximum minus daily minimum) of the PM₁₀ concentration (μg/m³) at 5 monitoring stations over 5 days.

Monitoring Station	2 January	3 January	4 January	5 January	6 January	Average Value for 5 Days
Station 11	134	94	99	120	147	119
Station 13	96	92	100	105	88	96
Station 15	105	69	77	91	76	84
Station 12	105	77	97	130	181	118
Station 14	153	131	156	111	117	134

that the range of PM_2.5 (daily maximum minus daily minimum) over this period is 61–149 μg/m³, and that of PM₁₀ is 61–181 μg/m³. The concentrations of PM_2.5 and PM₁₀ at the same site changed appreciably within 24 h on the same day. As shown in Figure 7 and Figure 8, there is no discernible consistent pattern in the PM_2.5 and PM₁₀ values, which differs from the O₃ data, and the high and low pollutant concentrations at each station within 24 h also varied widely on different days. The complex variation in PM_2.5 and PM₁₀ concentrations may be due to multiple factors, including uncontrollable emission sources, regional transport of pollutants, and meteorological conditions [22,25].

The differences between PM_2.5 and PM₁₀ concentrations in the three outdoor activity periods (6–9 a.m., 12–3 p.m., and 7–10 p.m.) were analyzed using ANOVA, and the results showed that the concentrations of PM_2.5 and PM₁₀ at night were significantly higher than those in the other two periods, as revealed in Figure 6b,c. Thus, reducing outdoor activities at night in winter could effectively reduce PM_2.5 and PM₁₀ exposure.

3.3. Monthly Pollutant Patterns and Seasonal Differences

Five air quality monitoring stations in Xi’an were selected to assess the monthly changes in O₃, PM₁₀, and PM_2.5. The daily average concentration value was used to calculate the monthly average concentration values for the 12 months in 2019. It can be seen from the entries in Figure 9a that the concentration of O₃ first increased and then decreased in the period from January to December 2019, with the highest value occurring in July. In Figure 9b,c, the concentrations of PM_2.5 and PM₁₀ exhibited an initial decrease and subsequent increase in the same period, with the highest value noted in January.

The concentrations of O₃, PM₁₀, and PM_2.5 in spring (March–May), summer (June–August), autumn (September–November), and winter (December–February) in Xi’an were analyzed using analysis of variance. The results showed that the concentration of O₃ in summer was significantly higher than that in other seasons (p < 0.001 ***), and the concentration of O₃ in spring was significantly higher than that in autumn (p < 0.001 ***) and winter (p < 0.001 ***), as revealed in Figure 10a. This finding can be attributed to the higher temperature in summer, which accelerates the photochemical reactions that generate O₃ [24,26]. The analysis results of PM_2.5 and PM₁₀ were similar, and the concentrations of PM_2.5 and PM₁₀ were significantly higher in winter than in other seasons (p < 0.001 ***) and significantly higher in spring and autumn than in summer (p < 0.001 ***), as revealed in Figure 10b,c. Given the appreciable pollution caused by winter heating in Xi’an, coupled with the unfavorable terrain and static weather, these pollutants cannot be diffused [27,28].

3.4. Spatial Patterns at Different Time Points

3.4.1. Differences in O₃ Spatial Distribution

Two time points, 7:00 for low concentration and 15:00 p.m. for high concentration, were selected for the 5-day period (3–7 August 2018). The daily average O₃ concentration data were used and interpolated as raster data, applying IDW with Arcgis 10.4. The raster data were extracted after interpolation according to the mask (central urban area) to obtain the O₃ spatial distribution for the central urban area of Xi’an. As shown in Figure 11, with the exception of the high O₃ concentration in the middle of the central urban area at 7:00 a.m. on 5 August, the concentration of O₃ in the northern part of the central urban area was much higher than that in other areas on the remaining four days. This serves to indicate that there were emission sources in the northern part of the central urban area that had not been effectively controlled [19,29]. Moving from 7:00 a.m. to 15:00 p.m. (Figure 12), there is no longer evidence of a high concentration of O₃ in the north of the central urban area, and this apparently had shifted to the middle of the central urban area. This can be ascribed to excessive emission of O₃ precursors, such as NOx and volatile organic compounds, from the dense pedestrian and vehicular traffic in the central urban area [30,31].

3.4.2. Differences in PM_2.5 and PM₁₀ Spatial Distributions

Three time points, 7:00 a.m., 12:00, and 18:00 p.m., were selected for five typical polluted days (2–6 January 2019), representing the morning, noon, and evening peaks, respectively. The spatial distributions of PM_2.5 and PM₁₀ were obtained using the same method as noted for O₃. The differences in the spatial PM_2.5 concentration distribution on different days are shown in Figure 13, Figure 14 and Figure 15. A comparison of the vertical images reveals differences in the spatial PM_2.5 concentration distribution at different time points on the same day. Differences for different days at the same time point are illustrated in Figure 16, Figure 17 and Figure 18. Likewise, a comparison of the vertical images establishes differences in the spatial PM₁₀ concentration distribution at different time points on the same day. An assessment of the horizontal images shows that PM_2.5 and PM₁₀ at the same time point also differ in terms of spatial distribution. However, in some areas, the concentration remained consistent for the 5-day period. For example, the concentrations of PM_2.5 and PM₁₀ in the west and southwest of the central city were consistently high, while in the southeast, these concentrations were uniformly low. The spatial distributions of PM_2.5 and PM₁₀ show obvious differences at different time points, but there are clearly high-concentrated areas [12,32]. Both the difference and consistency should be recognized and taken into account in developing a pollution control strategy.

3.5. Application and Limitation

This study found that the hour-by-hour concentration change in O₃ showed a consistent pattern of change with time as it was high at noon and low in the morning and evening. Thus, the government’s governance of O₃ pollution should be strengthened during the high-temperature hours for VOC emission source control. In the morning, low-temperature hours in the northern part of the central city form a high spatial concentration of O₃. In the afternoon, the high spatial concentration of O₃ shifted to the central part of the central city. The government’s policies should pay attention to the regional differences in the emission sources of VOCs and develop a targeted governance program. The hour-by-hour concentration changes in PM_2.5 and PM₁₀ do not show a consistent pattern of time change, and the governance regarding PM_2.5 and PM₁₀ should maintain the long-term nature of the policy. The spatial distributions of PM_2.5 and PM₁₀ at different points in time also show differences, but there are still persistent areas of high concentrations located in the western and southwestern parts of the central city. The treatment of these areas is crucial. To reduce pollution exposure from outdoor activities, the government can issue health reminders. These reminders should indicate that O₃ remains at high concentrations in the evening in summer and declines slowly, and outdoor activities should be postponed as much as possible. In addition, outdoor activities should be carried out in the morning in winter when evenings exhibit more severe PM_2.5 and PM₁₀ pollution than early mornings. Due to the limitations of the workload and the length of the article, we only selected 2–3 representative time points to analyze the spatial distribution of pollutants, and subsequent studies will analyze the spatial distribution of pollutants at more time points and on more days to reveal more patterns of change in the spatial distribution of air pollutants.

4. Conclusions

The temporal and spatial distribution characteristics of O₃, PM_2.5, and PM₁₀ in Xi’an, a heavily polluted city in Fenwei Plain, were analyzed. Changes to monthly, daily, and hourly concentrations and spatial distributions as well as the differences in concentrations during different seasons and at different time periods have been considered for typical polluted days. O₃ concentrations reflect the prevailing temperature with lower values at lower temperatures (7:00 a.m.) and higher concentrations at higher temperatures, i.e., 15:00 p.m. in summer. In winter, PM_2.5 and PM₁₀ exhibit a morning peak at 7:00 a.m. that dropped during periods of lower traffic flow (12:00), with an evening peak at 18:00 p.m. in winter. The results generated in this study support the following conclusions:

• The monthly changing pollutant pattern indicates that the O₃ concentration in summer is higher than thar in other seasons, with the highest concentration noted in July. The concentrations of PM_2.5 and PM₁₀ in winter are higher than those in other seasons, with the highest concentrations noted in January. ANOVA results show that due to the high temperatures in summer, the photochemical reaction rate of O₃ increases, leading to significantly higher O₃ levels in summer compared to other seasons. Additionally, in winter, the heating and stable weather conditions in Xi’an hinder pollution dispersion, resulting in PM_2.5 and PM₁₀ concentrations being significantly higher in winter than in other seasons.
• The daily changing pollutant pattern indicates less than 30 qualified days for PM_2.5 in Xi’an in 2019, according to the latest air quality standards of the WHO. The number of unqualified days for O₃ was greater than 100. This represents a potential risk of exposure to pollution with associated health risks. The government needs to formulate and implement more stringent air quality control policies, including limiting industrial emissions, strengthening traffic management, and promoting clean energy.
• The hourly pollutant pattern indicates that the concentrations of O₃, PM_2.5, and PM₁₀ at the same station varied widely within a 24-h period. O₃ concentrations remained at a low level from 0:00 to 8:00 a.m., increased from 9:00 a.m., and maintained a high level from 13:00 p.m. to 18:00 p.m. when it reached a maximum. In order to reduce exposure to O₃, outdoor activities should be avoided in the summer on sunny afternoons. Lower O₃ exposure during outdoor activities occurs in the morning compared with the evening, and outdoor activities should occur as late as possible in the evening. The patterns for PM_2.5 and PM₁₀ are quite different from O₃ over a 24-h period. The concentrations of PM_2.5 and PM₁₀ at night are significantly higher than that in the other two periods, so reducing outdoor activities at night in winter can effectively reduce PM_2.5 and PM₁₀ exposure. To prevent pollution exposure of urban outdoor populations, differentiated health risk alert programs and outdoor activity recommendations should be developed for different seasons, time periods, and types of pollutants.
• The spatial distribution of O₃ at 7:00 a.m. indicates emission sources in the north of the central urban area that are not effectively controlled. A high O₃ concentration is observed at the middle of the central urban area at 15:00 p.m. It is necessary to suppress the emission of O₃ precursors, such as NOx and volatile organic compounds, while paying attention to regional differences in emission sources and developing targeted treatment programs. The spatial distributions of PM_2.5 and PM₁₀ indicate significant differences at different time points, but a constant occurrence of high concentrated areas located in the western and southwestern parts of the central city was noted. The continued management of these areas will significantly improve the air quality in Xi’an. Due to the significant differences in the spatial distributions of O₃, PM_2.5, and PM₁₀, the observed differences and consistencies should be taken into account in any pollution control strategy.
• In the air quality ranking of 168 cities in China generated by the Ministry of Ecology and Environment of the People’s Republic of China, Xi’an ranked 165th and 164th in 2022 and 2023, respectively, with the top pollutant in summer being O₃, and the top pollutants in winter being PM_2.5 or PM₁₀. China’s three major heavily polluted regions include the Yangtze River Delta, the Beijing –Tianjin–Hebei region, and the Fenwei Plain, which is represented by Xi’an. Significant air quality improvements have been observed in the Yangtze River Delta and Beijing –Tianjin–Hebei regions. Winter particulate matter management has benefited from regional cooperation and joint prevention and control; industrial and energy structure optimization; enhanced mobile source management; and phased, targeted implementation. Effective measures for controlling summer O₃ include in-depth research on pollution sources and formation mechanisms; the development of non-linear coordinated control strategies for PM_2.5, O₃, VOCs, and NOx; unified deployment; and the establishment of a regional photochemical pollution monitoring network. These measures provide valuable experience for air pollution management in Xi’an.