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Article

Characteristics of Temporal and Spatial Changes in Ozone and PM2.5 and Correlation Analysis in Heilongjiang Province

by
Lichun Xuan
1,†,
Lei Li
2,†,
Pengjie Wang
2,*,
Yanfeng Xing
2,
Chengcheng Feng
2,* and
Rui Zhang
2
1
School of Chemistry and Materials Science, Heilongjiang University, Harbin 150080, China
2
Heilongjiang Ecological Environment Monitoring Center, Harbin 150056, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work and should be regarded as co-first authors.
Atmosphere 2023, 14(10), 1526; https://doi.org/10.3390/atmos14101526
Submission received: 24 August 2023 / Revised: 23 September 2023 / Accepted: 30 September 2023 / Published: 2 October 2023
(This article belongs to the Special Issue Advances in Air Quality Spatio-Temporal Mapping)
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Abstract

:
The escalating ambient ozone (O3) pollution in China has garnered significant attention, necessitating an intensified focus on O3 pollution control and the coordinated management of PM2.5 and O3. This study reviews and analyzes the spatiotemporal characteristics of O3 and PM2.5 concentrations in 13 cities within Heilongjiang Province from 2019 to 2021. The analysis is based on data sourced from the ecological environment monitoring network. In addition to this, correlation analyses were executed to explore the interaction between the two pollutants. The findings reveal a declining trajectory in PM2.5 concentration over the past three years, while O3 concentration has exhibited an upward trend. Temporally, both O3 and PM2.5 concentrations display pronounced seasonal variations, with peaks evident during the spring and summer (May to July), as well as in the winter (January, February, and December). From a spatial standpoint, elevated O3 concentrations were identified in the southwestern cities of Harbin, Daqing, and Suihua, while the northwestern cities of Daxinganling and Heihe exhibited comparatively lower O3 concentrations, but the difference was not significant. Conversely, PM2.5 concentrations demonstrated substantial variation among the 13 cities (districts). Regarding their correlation, a noteworthy positive correlation between the two pollutants was observed in April and May, contrasted by a negative correlation in November and December. Weather categories such as excellent, good, lightly polluted, moderately polluted, and other weather showed a lower correlation, whereas heavily polluted and severely polluted categories demonstrated a stronger correlation. Furthermore, the correlation with severe pollution is greater than that with heavily polluted, further indicating that heavier air pollution is more conducive to the coexistence of O3 and PM2.5 to form composite pollution. On a provincial scale, the correlation between the two pollutants is progressively increasing annually. This signifies a closely intertwined and intricate interaction and transformation relationship between O3 and PM2.5, accentuating the urgency for synergistic control measures.

1. Introduction

Ongoing economic development and urbanization have exacerbated atmospheric environmental pollution, which remains a pivotal environmental challenge for China. Currently, China grapples with regional composite pollution, primarily driven by ozone (O3) and PM2.5 [1,2,3], embodying prevailing atmospheric pollution attributes. This composite pollution significantly impedes a persistent improvement in our air quality, eliciting widespread concern and becoming the nucleus of the present atmospheric pollution control and an important direction of environmental management. Its impact on human health and the ecological environment is unignorable.
O3, classified as a potent oxidant [4], constitutes a secondary pollutant engendered via the photochemical reaction of volatile organic compounds (VOCs) and nitrogen oxides (NOx) released from pollution sources under solar irradiation. It is responsible for adverse effects on the human respiratory [5,6,7], immune [6], and cardiovascular systems [7], as well as the growth and development of flora [8,9]. Conversely, PM2.5 particulate constitutes minuscule particulate matter with a diameter not surpassing 2.5 μm, facilitating its entry into the human respiratory tract and subsequently the lungs. This ingress inflicts harm on the respiratory system and cardiovascular system [10,11,12,13]. Notably, it can be directly emitted from pollution sources (e.g., road dust, building construction dust, coal combustion dust) [14,15,16]. Alternatively, it can arise through atmospheric photochemistry, heterogeneous reactions, and other processes, yielding secondary formation (e.g., secondary sulfate, secondary nitrate, and secondary organic matter) [17,18,19,20].
Research indicates a multifaceted interaction between O3 and PM2.5. O3 profoundly influences the chemical mechanisms underlying PM2.5 formation [21,22,23,24]. Furthermore, near-surface O3 levels stimulate the production of secondary particulate matter (SPM) by modifying atmospheric oxidative characteristics [25,26]. However, diminished O3 concentrations do not favor the generation of SPM [27]. Likewise, PM2.5 impacts the production and mitigation of O3. Diminishing PM2.5 concentrations were found to be correlated with elevated ozone levels [28], chiefly due to PM2.5’s modulation of solar radiation and its influence on atmospheric heterogeneous reaction procedures. A decrease in PM2.5 concentration was found to be associated with a surge in ozone concentration [28], primarily attributed to PM’s role in influencing the near-surface O3 concentration through the modulation of solar radiation and alterations to atmospheric heterogeneous reaction processes [29,30,31].
Situated as a vital hub for food, forest resources, and energy in China, Heilongjiang Province occupies a strategic position as a northern frontier region, endowed with distinctive geographical and climatic attributes that give rise to ambient air pollution challenges. Presently, while the number of national inquiries into O3 and PM2.5 pollution traits and trends continues to rise, the displayed trends and configurations exhibit pronounced divergence across distinct areas. Hence, the deployment of distribution models from other regions cannot be straightforwardly extrapolated to depict the precise circumstances prevailing in Heilongjiang Province. This study comprehensively encompasses all prefecture-level cities within Heilongjiang Province. It scrutinizes the multifaceted changes in O3 and PM2.5 characteristics from 2019 to 2021, assessing the O3-PM2.5 correlation on an expansive scale and from diverse perspectives. The overarching goal is to furnish a scientific foundation for ameliorating the current composite pollution state of air quality in Heilongjiang Province. Moreover, this study strives to supply vital technical backing for orchestrating integrated O3 and PM2.5 management and regulation across cities, encompassing temporal and regional strategies. Additionally, this study extends substantial technical support for harmonized O3 and PM2.5 control endeavors.

2. Sources and Methods

2.1. Data Sources

In this paper, the geographical scope includes Heilongjiang Province, encompassing all cities above the prefecture level. The cities include Harbin, Qiqihar, Mudanjiang, Jiamusi, Daqing, Jixi, Shuangyashan, Yichun, Qitaihe, Hegang, Heihe, Suihua, and Daxinganling. The dataset originates entirely from air pollutant concentration data provided by the Ecological Environment Monitoring Centre of Heilongjiang Province, spanning from 2019 to 2021, and is expressed in micrograms per cubic meter (µg/m3). The dataset comprises hourly and daily measurements of O3 and PM2.5 concentrations. Additionally, month-to-month and year-to-year concentration data were acquired using rigorous statistical analysis and calculations. Notably, the O3 concentration represents the daily maximum 8 h sliding average, excluding hourly readings. For the purpose of this study, the seasonal divisions are defined as follows, March to May as spring, June to August as summer, September to November as autumn, and January, February, and December as winter.

2.2. Research Methodology

The data in this paper underwent processing and analysis using Excel, Argis, and Origin software. During the analysis process, linear regression analyses and the correlation coefficient method were used to investigate the relationship between O3 and PM2.5. The assessment of date was conducted based on the guidelines outlined in the Ambient Air Quality Standard (GB 3095-2012), Technical Regulation on Ambient Air Quality Index (AQI) (on trial) (HJ 633-2012), and Technical Specification for Ambient Air Quality Evaluation (on trial) (HJ 663-2013).
The ambient air quality is evaluated using the ambient air quality index (AQI). AQI is divided into six levels, “0 ≤ AQI ≤ 50” is “Excellent”, “50 < AQI ≤ 100” is “Good”, “100 < AQI ≤ 150” is “Lightly polluted”, “150 < AQI ≤ 200” is “Moderately polluted”, “200 < AQI ≤ 300” is “Heavily polluted”, and “AQI > 300” is “Severely polluted”. The maximum value of the individual air quality index (IAQI) for pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) is AQI. The IAQI was calculated using the following equations:
I A Q I P = I A Q I H i − I A Q I L o B P H i − B P L o   ( C p − B P L o ) + I A Q I L o  
where IAQIP is the IAQI of pollutant P. Cp is the concentration value of pollutant P, in µg/m3. BPHi is the high value of pollutant concentration limits similar to Cp in Table 1, in µg/m3. BPLo is the lower value of pollutant concentration limits similar to Cp in Table 1, in µg/m3. IAQIHi is the air quality sub-indices corresponding to BPHi in Table 1. IAQILo is the air quality sub-indices corresponding to BPLo in Table 1.

3. Results and Discussion

3.1. Time Variation Characteristics of O3 and PM2.5

3.1.1. Year-by-Year Change Characteristics

We identified the year-by-year situation by analyzing the changes in PM2.5 and O3 pollution days (refers to the days when the daily average IAQI of O3 and PM2.5 was greater than 100, excluding days with excellent and good IAQI) from 2019 to 2021. We calculated the number of days with different types of pollution days in 13 cities each year by adding them up separately. In 2019, Heilongjiang Province witnessed 33 days of O3 pollution days, out of which 32 days were classified as lightly polluted and one day as moderately polluted. Additionally, there were 244 days of PM2.5 pollution days, comprising 140 lightly polluted days, 37 moderately polluted days, 50 heavily polluted days, and 17 severely polluted days. The year 2020 recorded 40 days of O3 pollution days, with 36 days categorized as lightly polluted, and four days categorized as moderately polluted. Furthermore, there were 292 days of PM2.5 pollution days, including 184 lightly polluted days, 49 moderately polluted days, 40 heavily polluted days, and 19 severely polluted days. In 2021, the province observed 52 days of O3 pollution days, with 48 lightly polluted days and four moderately polluted days. Additionally, there were 188 days of PM2.5 pollution days, consisting of 139 lightly polluted days, 24 moderately polluted days, 22 heavily polluted days, and three severely polluted days. Evaluating the number of pollution days and shifts in air quality levels from 2019 to 2021 (Figure 1 and Figure 2), it becomes apparent that O3 pollution in Heilongjiang Province is deteriorating progressively. While the number of days with PM2.5 pollution days displays a declining trend, the fluctuations remain substantial, indicating a lack of consistent and substantial decrease. The persistently high number of pollution days underscores that PM2.5 continues to be the primary pollutant adversely impacting ambient air quality.

3.1.2. Month-by-Month Change Characteristics

As depicted in Figure 3, the monthly variation pattern in O3 in 13 cities across Heilongjiang Province from 2019 to 2021 exhibits a consistent inverted “U” shape. From the perspective of the average monthly concentration in Heilongjiang Province over the three years, O3 attained its peak in May at 133 μg/m3 and its nadir in December at merely 56 μg/m3, which is less than half of the peak value. The peak—trough pattern aligns with the nationwide changes. The monthly peaks and troughs in O3 in Heilongjiang Province mirror the national-scale trends. Specifically, O3 concentrations were low in January, escalated markedly from February to March, and culminated from April to July, predominantly from May to July. An overall decreasing trend was observed from August to December. The spring and summer emerge as peak seasons for O3 pollution in Heilongjiang Province, which is attributed to heightened temperatures, prolonged daylight, and increased light intensity. These factors promoted the photolysis of NOx and enhanced evaporation of VOCs, triggering redox reactions and resulting in accelerated ozone production and elevated O3 levels [32,33,34]. It is worth mentioning the significantly higher O3 concentration in the cities in Heilongjiang Province during 2020 and April 2021 compared with April 2019. These cities were affected by pandemic-induced restrictions on urban traffic and factory operations, coupled with relatively consistent meteorological conditions over the past three years. The surge in O3 concentration can be attributed to reduced emissions of NO and other ozone-depleting nitrogen oxides (NOx), which serve as “ozone depleting agents” [35], leading to diminished ozone depletion and consequently higher ozone concentrations. Temporally, strengthening precursor control from April to July—when O3 pollution is more severe—should be prioritized.
As can be seen in Figure 4, the monthly variation pattern in PM2.5 in 13 cities within Heilongjiang Province from 2019 to 2021 stands in contrast to that of O3, presenting an overall “U” shape. In terms of monthly average concentration, PM2.5 reached its peak in January at 56 μg/m3, surpassing the Grade 2 standard limit (35 μg/m3) defined in the “Ambient Air Quality Standard” (GB 3095-2012). Conversely, the lowest average concentration of PM2.5 was observed in August, registering 12 μg/m3, which is less than a quarter of the peak value. The concentration trend is highest in January and February, declining each month from March to August, then rebounding sharply from September to December. This disparity indicates higher PM2.5 concentrations during the winter compared with the summer, in accordance with nationwide urban trends. However, the variation in PM2.5 concentration in Heilongjiang Province is notably more pronounced. This disparity primarily stems from two factors. Firstly, the cold winters in Heilongjiang lead to increased consumption of coal and other energy sources for heating purposes. Some areas still use old household coal-fired boilers, contributing to dispersed air pollutant emissions that prove challenging to effectively mitigate [36]. Secondly, Heilongjiang Province experiences static inverse temperature and other unfavorable meteorological conditions during the winter. Prolonged periods of high humidity coupled with static atmospheric conditions hinder the horizontal and vertical diffusion of atmospheric pollutants, exacerbating the cumulative superposition effect and pollutant concentration [37].

3.1.3. Hour-by-Hour Change Characteristics

From 2019 to 2021, the hourly changes in the average O3 concentrations over the three years during different seasons across 13 cities (localities) in Heilongjiang Province are shown in Figure 5. The annual changes from 2019 to 2021 are shown in the Supplementary Materials (Figure S1). The figure demonstrates that the hourly trend in ozone concentrations remains largely consistent over the three-year period, displaying a discernible pattern. O3 concentrations are lower at night and in the early morning, gradually rising from 5:00 to 8:00 o’clock, peaking at 14:00 o’clock, and then exhibiting a gradual decline with diminishing amplitude until the trend stabilizes. However, variations in O3 hourly trends among the different seasons are notably distinct. In the spring and summer, O3 concentrations start to rise from 5:00 or 6:00 o’clock, while in the autumn and winter, this rise is delayed by two hours, commencing at 7:00 or 8:00 o’clock. The swift rise in morning ozone concentration is attributed to the gradual increase in solar radiation and the intensification of O3 photochemical reactions as the sun ascends. Additionally, the inclination of the Earth’s rotation axis contributes to extended sunshine durations to Heilongjiang Province during the spring and summer compared to autumn and winter. This discrepancy manifests as an earlier sunrise and later sunset, leading to earlier O3 concentration increases in the spring and summer. Conversely, the O3 concentration starts stabilizing around 18:00 or 19:00 o’clock during the autumn and winter, while this stabilization is notably delayed in the spring and summer, often approaching 0:00 o’clock. The influence of higher nighttime temperatures in the spring and summer accelerates the rate of O3 decay, resulting in a gradual decrease in ozone concentration. In addition, elevated nighttime temperatures lead to an increase in the height of the atmospheric stabilization layer, accelerating O3 diffusion and consequently reducing O3 concentration [38]. From an hourly perspective, if the hourly O3 concentration exceeds 160 μg/m3, swift actions such as organizing inspections for industries, automobile repair businesses, and catering establishments, encouraging nighttime refueling, and promoting eco-friendly commuting between 13:00 and 16:00 o’clock, should be implemented. These measures aim to reduce precursor emissions at the O3 source and subsequently lower the O3 concentration.
Figure 6 illustrates the hourly change of average over three years curves of PM2.5 during various seasons in 13 cities (districts) within Heilongjiang Province from 2019 to 2021. The annual changes from 2019 to 2021 are shown in the Supplementary Materials (Figure S2). The figure reveals consistent trends in seasonal changes over the past three years. The spring and autumn exhibit an overall shallow “U” shape, while the summer exhibits relatively stable changes. On the other hand, the winter exhibits a distinctive “W” shape, with the most substantial hourly variation among the four seasons. Notably, the PM2.5 concentration peaks between 9:00 and 10:00 o’clock and again between 21:00 and 22:00 o’clock during the winter. The morning peak is primarily influenced by anthropogenic factors like transportation and residential heating, while the nocturnal peak is more driven by industrial emissions and heating processes. Moreover, across all four seasons, a PM2.5 trough is observed at 15:00 o’clock, followed by a rapid rise culminating around 22:00 o’clock. Beyond the effects of traffic during evening rush hours from 18:00 to 20:00 o’clock, the lower boundary layer height and atmospheric layer stability during the night restrict the vertical dispersion of pollutants, contributing significantly to the nighttime increase in PM2.5 concentration.

3.2. Spatial Variation Characteristics of O3 and PM2.5

The spatial distribution characteristics of the annual mean O3 concentration within Heilongjiang Province from 2019 to 2021 are shown in Figure 7. The ozone concentration in different regions of Heilongjiang Province exhibits variable patterns. Notably, the southern region (Harbin, QiQihaer, Daqing, and Suihua) experiences evident ozone pollution, while the northeastern region (Jiamusi, Jixi, Shuangyashan, Yichun, Qitaihe, and Hegang) displays relatively higher concentrations. The northwestern region (Heihe, Daxinganling), meanwhile, records the lowest average concentrations. On a local scale, minimal disparity in O3 concentrations is observed among cities within each region, emphasizing the regionalized nature of O3 pollution with pronounced transport characteristics [39,40,41,42,43].
The spatial distribution characteristics of the annual mean PM2.5 concentration within Heilongjiang Province from 2019 to 2021 are depicted in Figure 8. In comparison with the regional variability shown by O3, the variability in PM2.5 concentration is more pronounced. While the mean value indicates a sequence of southern region > northeastern region > northwestern region, substantial differences in high and low PM2.5 concentrations exist among cities within each region. Notably, the difference in PM2.5 concentrations is more significant between Harbin and Qiqihar. Consequently, it is apparent that PM2.5 has weak transmission capacity [43,44,45], with a relatively limited spatial distribution range, especially under static and stable weather conditions, predominantly affected by local regional pollution sources.
Urbanization progress is notably more rapid in the southern region compared with the northern region. The southern region grapples with high population density and pronounced traffic pollution issues, especially in the capital city of Harbin, which witnesses a substantial number of motor vehicles and proportionately higher exhaust emissions. Qiqihar serves as a key petrochemical base, and Daqing plays a significant role in oil production. Consequently, these industrial activities inevitably yield a certain concentration of pollutants that affect air quality. In contrast, the northwestern and northeastern regions experience lower O3 and PM2.5 concentrations due to flatter topography, lower average altitude, low population density, and reduced industrial pollution. Additionally, the colder climate and lower summer temperatures in the northwest hinder favorable conditions for O3 generation, leading to the lowest O3 concentrations.
Geographically, considering O3’s regional behavior, enhancing the collaborative prevention and control mechanism for O3 pollution across provincial cities is crucial. Furthermore, refining the response strategy for heavily polluted PM2.5 weather conditions in key areas is essential to comprehensively enhance air quality.

3.3. O3 and PM2.5 Correlation Analysis

3.3.1. Year-by-Year Correlation Analysis

As observed in Figure 9, while the overall correlation between the annual mean values of O3 and PM2.5 from 2019 to 2021 is modest: they all demonstrate a positive correlation and display an increasing trend year by year. Specifically, the correlation coefficients are 0.5757, 0.6072, and 0.6756 in 2019, 2020, and 2021, respectively. It is particularly noteworthy that from 2020 to 2021, the correlation coefficient increased by nearly 0.07, which is twice the increase from 2019 to 2020, indicating a progressively closer and tighter correlation between O3 and PM2.5.

3.3.2. Month-by-Month Correlation Analysis

Figure 10 illustrates that the correlation between O3 and PM2.5 varies significantly among months, with the highest correlations occurring in the summer, followed by the spring, autumn, and winter. The average correlation coefficient for all 12 months is 0.485, with the peak correlation coefficient of 0.8814 observed in June. Notably, May also demonstrates a strong correlation coefficient of 0.7444 due to its high ozone concentration, far exceeding the average level. Conversely, January, November, and December show a negative correlation between O3 and PM2.5. Overall, a positive and strong correlation is evident between O3 and PM2.5 in months characterized by high ozone concentration and low PM2.5 concentration, while a negative correlation is prevalent in months with low ozone concentration and high PM2.5 concentration. Consistent with the yearly analyses, the annual mean ozone values increase annually, PM2.5 concentrations decrease yearly, and O3 and PM2.5 correlations intensify yearly.

3.3.3. Correlation Analysis of Different air Quality Categories

Distinctly visible from Figure 11 is the low correlation between the different O3 and PM2.5 air quality categories, particularly evident in situations of good, light pollution, and moderate pollution. Presently, the primary pollutant influencing ambient air quality in Heilongjiang Province is PM2.5, accounting for 79.0% of days exceeding limits, while O3 contributes to 16.7% of days exceeding limits, which is a notably lower proportion compared with PM2.5. Upon analyzing monthly correlations, it becomes apparent that while high PM2.5 concentration months exhibit low or even negative correlations between O3 and PM2.5, heavily polluted and severely polluted cases present a positive correlation between the two. This emphasizes that under certain levels of PM2.5 concentration, O3 generation can be promoted, likely due to common pollution sources giving rise to both PM2.5 and ozone precursor emissions, thus leading to increased O3 concentration.

4. Conclusions

In this study, we reviewed and analyzed the spatiotemporal characteristics of O3 and PM2.5 concentrations in Heilongjiang Province and found that the spatial and temporal variations in O3 and PM2.5 in Heilongjiang Province exhibit distinct characteristics. From a time perspective, there is a significant difference in the concentration changes in O3 and PM2.5, especially for the opposite trend in monthly changes. From a spatial perspective, the results reveal that O3 and PM2.5, both atmospheric pollutants, exhibit varying regional transport behaviors, generation conditions, and pollution sources. O3 primarily forms through photochemical reactions involving VOCs and NOx under intense sunlight and high temperatures. It possesses substantial potential for long-distance transport. In contrast, PM2.5 arises predominantly from direct emissions via combustion activities, road dust, industrial activities, and other sources or via secondary formation. Its transmission range is comparably limited.
The correlation between O3 and PM2.5 is higher during high ozone concentration periods, and during the air quality pollution period, it significantly surpasses the correlation during the non-pollution period, particularly during episodes of heavy and severe pollution. Additionally, this correlation progressively increases year by year. On one hand, the presence of pollutants like nitrogen oxides (NOx) influences O3 generation, accelerating the accumulation of PM2.5 in polluted environments. On the other hand, organic and inorganic substances within PM2.5 serve as precursors for O3, contributing to its generation. Consequently, the intricate relationship between O3 and PM2.5 necessitates acknowledgment, emphasizing the requirement for effective pollution control strategies that account for their interactions, characteristics, and influencing factors.
Source-wise, expediting source analysis for O3 and PM2.5 is recommended. Researching the mechanisms and coordinating control countermeasures for their formation should be pursued. Undertaking projects focused on the formation mechanism of O3 and PM2.5, along with their coordinated control, is vital. Completing emission source inventories and expediting the establishment of such inventories for both the province and individual cities will facilitate the formulation of targeted control strategies. This data-driven approach will provide a scientific basis for informed decisions regarding the advancement of coordinated control and joint prevention of O3 and PM2.5, fostering inter-regional coordination and protection of public health and living environments.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atmos14101526/s1, Figure S1. Trends in monthly mean concentrations of O3 in 13 cities in Heilongjiang Province in 2019 (a), 2020 (b) and 2021 (c); Figure S2. Trends in monthly mean concentrations of PM2.5 in 13 cities in Heilongjiang Province in 2019 (a), 2020 (b) and 2021 (c).

Author Contributions

Conceptualization, P.W. and C.F.; data curation, L.X. and P.W.; formal analysis, P.W. and C.F.; investigation, R.Z.; methodology, L.L. and Y.X.; resources, L.L.; software, R.Z.; visualization, Y.X.; writing—original draft, P.W. and C.F.; writing—review and editing, L.X. and Y.X. All authors have read and agreed to the published version of this manuscript.

Funding

This research did not receive a specific grant from any funding agencies in the public, commercial, or non-for-profit sectors.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Trends in the number of days of O3 pollution in different categories in Heilongjiang Province, 2019–2021.
Figure 1. Trends in the number of days of O3 pollution in different categories in Heilongjiang Province, 2019–2021.
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Figure 2. Trends in the number of days of PM2.5 pollution in different categories in Heilongjiang Province, 2019–2021.
Figure 2. Trends in the number of days of PM2.5 pollution in different categories in Heilongjiang Province, 2019–2021.
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Figure 3. Trend in the monthly average concentration of O3 in 13 cities in Heilongjiang Province, 2019–2021.
Figure 3. Trend in the monthly average concentration of O3 in 13 cities in Heilongjiang Province, 2019–2021.
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Figure 4. Trend in the monthly average concentration of PM2.5 in 13 cities in Heilongjiang Province, 2019–2021.
Figure 4. Trend in the monthly average concentration of PM2.5 in 13 cities in Heilongjiang Province, 2019–2021.
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Figure 5. Trends in the monthly mean concentrations of O3 in 13 cities in Heilongjiang Province.
Figure 5. Trends in the monthly mean concentrations of O3 in 13 cities in Heilongjiang Province.
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Figure 6. Trends in the monthly mean concentrations of PM2.5 in 13 cities in Heilongjiang Province.
Figure 6. Trends in the monthly mean concentrations of PM2.5 in 13 cities in Heilongjiang Province.
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Figure 7. Changes in the annual average concentrations of O3 in 13 cities in the province, 2019–2021: the background color of the map is the difference between the average values of the three years.
Figure 7. Changes in the annual average concentrations of O3 in 13 cities in the province, 2019–2021: the background color of the map is the difference between the average values of the three years.
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Figure 8. Changes in the annual average PM2.5 concentrations in 13 cities in the province, 2019–2021: the background color of the map is the difference between the average values of the three years.
Figure 8. Changes in the annual average PM2.5 concentrations in 13 cities in the province, 2019–2021: the background color of the map is the difference between the average values of the three years.
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Figure 9. O3 and PM2.5 correlation analysis for 2019 (a), 2020 (b) and 2021 (c).
Figure 9. O3 and PM2.5 correlation analysis for 2019 (a), 2020 (b) and 2021 (c).
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Figure 10. Correlation analysis between O3 and PM2.5, January–December 2019–2021.
Figure 10. Correlation analysis between O3 and PM2.5, January–December 2019–2021.
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Figure 11. Correlation analysis between O3 and PM2.5 for different air quality categories.
Figure 11. Correlation analysis between O3 and PM2.5 for different air quality categories.
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Table 1. Individual air quality index (IAQI) and corresponding pollutant concentration limits.
Table 1. Individual air quality index (IAQI) and corresponding pollutant concentration limits.
IAQIO3 Concentration (µg/m3)PM2.5 Concentration (µg/m3)
000
5010035
10016075
150215115
200265150
300800250
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MDPI and ACS Style

Xuan, L.; Li, L.; Wang, P.; Xing, Y.; Feng, C.; Zhang, R. Characteristics of Temporal and Spatial Changes in Ozone and PM2.5 and Correlation Analysis in Heilongjiang Province. Atmosphere 2023, 14, 1526. https://doi.org/10.3390/atmos14101526

AMA Style

Xuan L, Li L, Wang P, Xing Y, Feng C, Zhang R. Characteristics of Temporal and Spatial Changes in Ozone and PM2.5 and Correlation Analysis in Heilongjiang Province. Atmosphere. 2023; 14(10):1526. https://doi.org/10.3390/atmos14101526

Chicago/Turabian Style

Xuan, Lichun, Lei Li, Pengjie Wang, Yanfeng Xing, Chengcheng Feng, and Rui Zhang. 2023. "Characteristics of Temporal and Spatial Changes in Ozone and PM2.5 and Correlation Analysis in Heilongjiang Province" Atmosphere 14, no. 10: 1526. https://doi.org/10.3390/atmos14101526

APA Style

Xuan, L., Li, L., Wang, P., Xing, Y., Feng, C., & Zhang, R. (2023). Characteristics of Temporal and Spatial Changes in Ozone and PM2.5 and Correlation Analysis in Heilongjiang Province. Atmosphere, 14(10), 1526. https://doi.org/10.3390/atmos14101526

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