Concentrations of Four Major Air Pollutants among Ecological Functional Zones in Shenyang, Northeast China

: Air pollution is a critical urban environmental issue in China; however, the relationships between air pollutants and ecological functional zones in urban areas are poorly understood. Therefore, we analyzed the spatiotemporal characteristics of four major air pollutants (particulate matter less than or equal to 2.5 μ m (PM 2.5 ) and 10 μ m (PM 10 ) in diameter, SO 2 , and NO 2 ) concentrations over five ecological functional zones in Shenyang, Liaoning Province, at hourly, seasonal, and annual scales using data collected from 11 monitoring stations over 2 years. We further assessed the relationships between these pollutants and meteorological conditions and land ‐ use types at the local scale. Peaks in PM, SO 2 , and NO 2 concentrations occurred at 08:00–09:00 and 23:00 in all five zones. Daytime PM concentrations were highest in the industrial zone, and those of SO 2 and NO 2 were highest in residential areas. All four air pollutants reached their highest concentrations in winter and lowest in summer. The highest mean seasonal PM concentrations were found in the industrial zone, and the highest SO 2 and NO 2 concentrations were found in residential areas. The mean annual PM and SO 2 concentrations decreased in 2017 in all zones, while that of NO 2 increased in all zones excluding the cultural zone. The natural reserve zone had the lowest concentrations of all pollutants at all temporal scales. Pollutant concentrations of PM 2.5 , PM 10 , SO 2 , and NO 2 were correlated with visibility, and their correlation coefficients are 0.675, 0.579, 0.475, and 0.477. Land coverage with buildings and natural vegetation negatively and positively influence air pollutant concentrations, respectively.


Introduction
Air pollution has detrimental effects on human health [1][2][3], atmospheric visibility [4,5], air quality [6,7], and regional and global climate change [8,9]. Seven of the 10 most-polluted cities in the world are in China [10], and air quality in China has been extremely poor in recent decades. To combat this problem, environmental monitoring stations have been established in most Chinese cities to monitor pollution and inform control measures. These data have been used extensively to describe the spatiotemporal characteristics of air pollutants [11][12][13][14][15]. Data collected from 2006 to 2014 at 24 stations indicated that particulate matter (PM) concentrations were highest in Xi'an, Zhengzhou, and Gucheng, followed by cities in Northeast and South China [16]. Zhao et al. [14] found that, within China, cities located in the North China Plain were the most severely polluted, and SO2 concentrations were highest in the northern part of the country. As for NO2, however, there was no significant difference between northern and southern Chinese cities [12]. Generally, air pollution is higher in winter and lower in summer [17][18][19][20]. However, pollution characteristics vary among cities. For instance, particulate matter with a diameter < 2.5 μm (PM2.5) concentrations in northern Chinese cities exhibited bimodal annual trends [14], whereas no pattern was observed in southern Chinese cities [14].
Although the emissions are the main cause of high concentrations, the influence of meteorological conditions and human activities cannot be ignored because both are important for the dispersion and transformation of air pollutants. Generally, strong wind speed favors the dispersion of air pollutants [21]. Additionally, temperature [22], relative humidity [23], wind direction [24], temperature inversion [25], and so on are related to dispersion and transformation of air pollutants. On the other hand, the intricate feedback loop between urban heat island, temperature-driven chemistry and urban pollution, urbanization also plays an important role for the air quality [26]. Land cover [27], high population density [28], and heavy traffic [29] all can affect pollutants' concentrations. Rendón et al. [30] simulated the impact of increasing urban land cover from 0% (rural) to 100% (urbanscape) on urban pollutants, and it indicated that the 40% urban cover was the most penalized in terms of air quality due to the combined effect of high urban emissions and temperature inversion persistence [31].
Shenyang, the capital of Liaoning Province, Northeast China, is undergoing rapid economic expansion and urban population growth. Air pollution is a major concern, with frequent air pollution events that can be severe. Famously, in November 2015, the maximum hourly PM2.5 concentration exceeded 1000 μg•m −3 (http://news.sina.com.cn/c/nd/2015-11-08/doc-ifxknutf1607479) in Shenyang. Studies designed to inform the development of control methods, including pollution monitoring [32,33], transport paths [34], and compounding factors [29], have been carried out in Shenyang. Although numerous studies have investigated air pollution in the city, there are few analyses of pollutant characteristics across ecological functional zones [35,36].
To address this limitation, we classified the ecological functional zones at 11 established monitoring stations in Shenyang and compared the variations in four major air pollutants (PM2.5, PM10, SO2, and NO2) among these zones over three time scales (hourly, seasonal, and annual). The aims of this research were to (1) analyze temporal variations in PM, SO2, and NO2 concentrations in five ecological functional zones in Shenyang; (2) understand differences in PM, SO2, and NO2 concentrations among these zones; (3) determine local relationships of air pollutants with meteorological elements and land-use types, with the goal of providing the local government with information and suggestions for future pollution control measures.

Study Area
Shenyang, the capital city of Liaoning Province in the northeastern China, is an industrial city with a population of more than 8 million [37,38]. The topography is generally flat, and the city sits at < 50 m above sea level. The Hunhe River flows across the southern urbanized area. The climate is subhumid continental with a hot, rainy season from June to August [39].

Air Pollution, Meteorological, Urbanization, and Imaging Data
Hourly measurements of PM2.5, PM10, SO2, and NO2 concentrations at all stations collected over a 2-year period (1 January2016 to 31 December 2017) were obtained from Shenyang Environment Monitoring Center for use in this study. Corresponding hourly meteorological data from the Shenyang weather station ( Figure 1) were obtained from the Liaoning Provincial Meteorological Information Center, including relative humidity, wind speed, and visibility. Urbanization data, such as population and number of motor vehicles, were obtained from various internet sources (https://tieba.baidu.com/p/4936711038?red_tag=2949498218). We classified all land-use types surrounding the monitoring stations based on Landsat Thematic Mapper images from 2015 with a resolution of 30 m. Images were obtained from the United States Geological Survey (http://glovis.usgs.gov). We used a preprocessing method for the Landsat images as described in Li et al. [37]. The ERDAS Imagine software and the ArcGIS software were also used in this study. Data used for classification included remotely sensed image, road maps, and field surveys. At last, land use was categorized into six types: buildings, roads, grassland, cropland, water, and bare land, with a combination of visual interpretation and supervised classification methods.

Ecological Function Zones
There were 11 pre-existing air quality monitoring sites in Shenyang ( Figure 1, Table 1). An ecological function zone was assigned to each station based on characteristics of the surrounding environment and geographical position when it was first established. The ecological function zone in this paper refers to the ecological function of the area where the monitoring site is located. The specific process how to determine ecological functional zone is as follows: (1) we made a 3 km circular buffer around each site, and then we counted the areas of main land use types and typical ecological function within each buffer (Table 1). (2) The 11 monitoring sites were divided into five ecological function zones. Firstly, the natural reserve and non-natural reserve were determined based the percentages of main land use areas within 3 km buffer of each station. The Senlinlu (SLR) station is located in the natural reserve and its green land area accounted for 99% within the buffer, so we determined it as the natural reserve zone (Z5). Secondly, the other stations were divided into residential-commercial zone (Z1), residential zone (Z2), cultural zone (Z3), and industrial zone (Z4) according to whether there was a flourishing business street, a concentrated residential block, a local famous university, industrial park in the corresponding zone, respectively. The classification results are shown in Table 1. Table 1. Eleven air quality monitoring sites in Shenyang, Liaoning Province. Height refers to the height of the sampling port from the ground. The number in the brackets is the percentage of land use area within 3 km buffer.

Diurnal Variations in Four Major Air Pollutants among Ecological Functional Zones
The hourly air pollutant concentrations in all five ecological functional zones are shown in Figure 2. Hourly PM2.5 concentrations were lowest in the natural reserve zone (Z5), and were 4.96-19.47 μg•m −3 lower than the concentrations found in other zones (Figure 2a,b). PM2.5 concentrations were consistently higher in the industrial zone (Z4) relative to the other zones. Feizizadeh et al. [28] also indicated that the highest PM concentration occurred in the petrochemical industrial site of Tabriz of Iranian. Among the four anthropogenic zones, the cultural zone (Z3) had the lowest levels of all pollutants. The variations in pollutant concentrations among the zones indicated that pollution was more problematic and variable in zones that contained a source of anthropogenic emissions (i.e., Zones 1-4) than in the zone without an anthropogenic emissions source (Z5). Average hourly PM2.5 concentration exhibited a bimodal distribution in all five zones in both 2016 and 2017 (Figure 2a,b). This is consistent with observations in other cities in Northeast China such as Siping and Anshan, but not with cities in the south such as Changde and Panyu [16]. Peaks in PM2.5 concentrations were less obvious in Z5 relative to the other zones, but peak values in all zones occurred between 08:00 and 09:00. Lows (valleys) in concentrations were generally observed at 15:00 in 2016 and at 16:00 in 2017 in all zones. These hours correspond to the variation in the diurnal boundary layer and to morning and evening rush hours [29]. The trends in hourly PM10 concentration across all zones in both years were consistent with those observed for PM2.5, excluding some small local fluctuations (Figure 2c,d).
Among all zones, average diurnal SO2 concentrations were highest in the residential zone (Z2), excluding a few hours in the morning when the average concentration was highest in Z4 ( Pronounced diurnal variations in SO2 concentration were observed in all five zones. Zones with sources of anthropogenic emissions had similar trends, with two peaks and one valley. This is different from most cities and regions of China such as in Lanzhou [40], Guangzhou [41], and Sichuan Basin [42] where there is only one peak for variation of diurnal SO2 concentrations. However, in Z5, the only zone without a source of anthropogenic emissions, only one obvious peak was observed (Figure 2e,f). The above variation characteristics mean that SO2 pollution situation is complex in Shenyang that it is an old and famous base for heavy industry. In Zones 1-4, the first peak in SO2 concentrations occurred at 08:00, whereas concentrations peaked at 10:00 in Z5. This suggests that SO2 emitted in urban areas (Zones 1-4) takes up to 2 h to spread to suburban areas (Z5).
The average hourly variations in NO2 showed a similar pattern to PM concentrations ( Figure  2g,h), including the timing of peaks and valleys. However, the greatest NO2 concentrations were observed in Z2 due to high vehicle traffic with increasing private cars, as opposed to Z4 for PM concentrations. There was no observed spike in NO2 concentrations during the day in Z5, likely because Z5 has very low vehicle traffic.

Seasonal Variations in Four Major Air Pollutants among Ecological Functional Zones
Average seasonal PM2.5 concentrations were highest in Z4 (Figure 3a), suggesting that industrial emissions were the main source of PM2.5 in Shenyang. Z3 had the lowest concentrations among zones with sources of anthropogenic emissions. In 2016, concentrations were highest in Z4 in summer and autumn, and in the residential-commercial zone (Z1) in spring and winter. Concentrations were substantially lower in Z5 than in all other zones over both years. We observed differences between zones with sources of anthropogenic emissions; the within-season variations were relatively small in 2016 (differences < 5.7 μg•m −3 ) and greater in 2017 (differences < 10.3 μg•m −3 ). . Higher SO2 concentrations in winter are a result of coal combustion during the heating season, which is consistent with most cities of north China [14]. The local government stipulates that each household must have an indoor temperature of at least 18 °C from November 1 to March 31 in Shenyang.
The variations in mean seasonal NO2 concentrations were similar to those observed for PM and SO2, i.e., highest in winter and lowest in summer (Figure 3d). In both years, the highest NO2 concentrations were found in Z2 during winter. Concentrations were often high in Z2; this is potentially related to an increase in the number of family cars owned by residents of Shenyang in recent years (https://tieba.baidu.com/p/4936711038?red_tag=2949498218). Figure 4 presents the hourly variation coupled with seasonal variation for all five zones and four air pollution components over both study years. Z5 had the lowest concentrations of all air pollutants. Z1 and Z2 showed similar concentrations across all time scales. Trends in the diurnal variation of PM2.5 were similar among zones (Figure 4a,b). Peaks in PM2.5 mostly occurred between 07:00 and 09:00 and between 23:00 and 00:00, excluding the winter and spring of 2017. Valleys occurred late at night and in the afternoon at approximately 14:00. Overall, hourly concentrations were smoother in the summer relative to the other seasons, which had sharp increases and decreases. Excluding spring, variations in PM10 (Figure 4c,d) concentrations were consistent with those of PM2.5. Z2 had the highest hourly SO2 concentrations, especially during winter, when the maximum concentration reached 137.78 μg•m −3 (Figure 4e,f). Mean hourly SO2 concentrations followed a bimodal pattern in Zones 1-4 during both spring and winter, but no discernable trend was observed in autumn. The first peak in the bimodal trend occurred at 08:00 and the second at 00:00. During summer, SO2 concentrations differed most among the four zones with a source of anthropogenic emissions between 09:00 and 20:00.
Mean hourly NO2 concentrations were consistently highest in Z2 in winter (Figure 4g,h). Concentrations showed a bimodal distribution in all seasons (excluding Z5 in winter), with the highest values occurring in the first peak in spring, autumn, and winter, and relatively similar peak concentrations in summer.
Broadly, most hourly air pollutant concentrations showed a bimodal distribution, with peaks during morning rush hour (09:00) and between 23:00 and 00:00. The lowest values were observed in the afternoon around 16:00. Peaks and valleys in concentrations are related to human activities and the height of the atmospheric boundary layer [16]. The time elapsed between the morning peak to the afternoon valley was 7 h, as was the gap between the afternoon valley and the midnight peak. However, only 5 h elapsed between the midnight peak and the morning rush hour peak the following day. This suggests that the diffusion lengths of pollutants were approximately 7 and 5 h under increased and decreased anthropogenic activity, respectively. These diffusion lengths were likely related to meteorological conditions. Wang et al. [16] suggested that meteorological factors play a major role in the short-term variation of air pollutant concentrations. In this study, most hourly mean concentration curves followed a unimodal pattern in summer. These findings are consistent with those of Li et al. [29], which suggested that unimodal distributions are linked to high relative humidity and low wind speeds at night in the summer months.

Annual Variations in Four Major Air Pollutants among Ecological Functional Zones
The mean annual PM2. 5   Overall, PM2.5 concentrations decreased significantly from 2016 to 2017 in all five functional zones (Table 2) Table 2).
The observed decreases in major air pollutants in Shenyang, a typical industrial city, are a result of recent emission reduction policies such as the Plan of Resistance to Haze and the Blue Sky Protection Campaign. Wang et al. [44] suggested that PM concentrations are decreasing due to the progress in emissions control made in China. Although our results indicate that industrial emissions can be controlled in Shenyang, NO2 concentrations increased from 2016 to 2017, a product of the increased use of motor vehicles, which are a source of NO2. In the past 10 years, government policies have been developed to encourage the purchase of family cars. The number of motor vehicles in Shenyang doubled from 2010 to 2017, and exceeded 2 million by 2017 (https://tieba.baidu.com/p/4936711038?red_tag=2949498218).

Meteorological Factors and Major Air Pollutants
Meteorological factors play an important role in the diffusion of air pollutants [45][46][47][48]. The 11 monitoring stations used in this study were all in close proximity (< 20 km apart), excluding the Senlin Road station (SLR). At this scale, the local microclimate affects pollution concentrations recorded at different stations. Given that no corresponding microclimate data were available for each station, we selected the closest environmental monitoring station (Hunnandong road station), located in Z1, to examine the relationships among meteorological factors and air pollutant concentrations.
The relationships between visibility and mean daily PM10, PM2.5, and NO2 concentrations were best represented by negative exponential curves ( Figure 5). However, the relationship between visibility and the mean daily SO2 concentration was linear, with a correlation coefficient of 0.512 (Figure 5c). Li et al. [29] indicated that fine particles can strongly constrain visibility via the atmospheric extinction effect, which is supported by these results (Figure 5d).
The relationships between pollutant concentrations and wind speed were similar to those with visibility, but the correlation coefficients (a measure of fit) of these relationships were weaker ( Table  3). The relationships between pollutant concentrations and relative humidity were best represented by binomial curves, suggesting complex relationships. Wang et al. [49] reported that the contribution of relative humidity to visibility becomes increasingly important with increasing PM2.5 concentrations, suggestive of interactive effects.

Land-Use Types and Major Air Pollutants
The land-use types of the area around each station were determined by creating circular buffers with a 3-km radius around each of the 11 monitoring stations. We classified the land-use types and calculated the area covered by each type within the buffer area ( Figure 6). The station SLR was located in a natural reserve, and the only land-use type around this station was green land (i.e., grassland or cropland). Therefore, we excluded this station from analyses of the relationships between land-use type and air pollutant concentration. At local scale, anthropogenic activities and their urban distribution play an important role for urban pollution [26]. Junk et al. [50] indicated that land use and its interaction with other factors can influence air quality. Thus, we analyzed the relationship between land-use types and air pollutants.  In contrast, green land area was significantly negatively correlated with air pollutant concentrations (p < 0.01 for all four pollutants), meaning that green land can reduce air pollution to some extent. This is a product of the capacity of plants to absorb air pollutants. The concentrations of air pollutants measured at SLR, located in Z5, were the lowest among all 11 environmental monitoring stations, providing a clear example of this relationship.
Four of the five zones contained sources of anthropogenic emissions. Among these, the temporal variations in the four air pollutants were similar; however, Z5, which did not contain an emission source, displayed some contrary trends. PM10 and PM2.5 concentrations were highest in Z4, and SO2 and NO2 concentrations were highest in Z2. The lowest observed values for all pollutants were found in Z5. Morning rush hour was associated with peaks in all four pollutants, and lows were observed around 16:00. Pollutant concentrations were typically highest in winter and lowest in summer. Mean annual concentrations of PM2.5, PM10, and SO2 decreased from 2016 to 2017, while NO2 increased. We suggest that the atmospheric conditions in Shenyang are improving annually in both developed areas and within reserve zones.
Visibility was exponentially and negatively related with PM10, PM2.5, and NO2, as was wind speed, albeit to a weaker degree. Visibility and SO2 had a linear relationship. Binomial relationships were observed between relative humidity and air pollutants. Government policy has had a significant influence on preventing and controlling air pollution. On a small scale, land-use type also has an important role in the diffusion and transportation of pollutants.
Based on the daily fluctuations in air pollutant concentrations, we suggest that morning and evening exercise may lead to greater pollutant inhalation, which may be unfavorable to human health. Therefore, we propose that 16:00 is the best time to exercise in Shenyang. Future city planning may impact air quality at local scales by increasing the amount of green space within urban areas, and by reducing the height and density of large buildings in urban areas.