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Article

The Interaction between Anthropogenic Activities and Atmospheric Environment in North China

by
Lanlan Yao
1,2,
Ruirui Si
3,4,*,
Wenyu Zhang
5,* and
Yanling Guo
3
1
School of Management, Lanzhou University, Lanzhou 730000, China
2
Editorial Department of Journal of Lanzhou University (Social Sciences), Lanzhou University, Lanzhou 730000, China
3
Henan Key Laboratory of Agrometeorological Support and Applied Technique, China Meteorological Administration, Zhengzhou 450003, China
4
Henan Provincial Climate Centre, Zhengzhou 450003, China
5
School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(5), 4636; https://doi.org/10.3390/su15054636
Submission received: 30 January 2023 / Revised: 13 February 2023 / Accepted: 3 March 2023 / Published: 5 March 2023
(This article belongs to the Section Air, Climate Change and Sustainability)
Browse Figures
Versions Notes

Abstract

:
To quantify the interaction between anthropogenic activities and the atmospheric environment in North China, spatiotemporal characteristics, transmission, source apportionment and a health risk assessment of the elements were analyzed in regional and background atmospheric research stations during a period of 2017. This study found that the concentration of PM2.5 and 16 elements in the regional station was 1.5 and 2.8 times higher, respectively, than that in the background station. Under the combined influence of human activities and the dusty weather in Spring, the concentration of 16 elements in the regional station was up to 3 times that in the background station. In terms of the transmission of PM2.5, the potential source regions of PM2.5 in North China were mainly the central and southern parts of Beijing-Tianjin-Hebei (BTH), as well as parts of northern Henan and western Shandong. The source apportionment of the elements proved that the elements in North China were mainly derived from soil dust (29.9–68.2%), followed by traffic (8.8–26.3%), with coal combustion and oil consumption accounting for 5.8–24.5% and 4.1–12.9%, respectively. Although the proportion was not the largest, traffic posed the highest health risk to people, which should draw the attention of the relevant authorities.

1. Introduction

Numerous scientists in recent years have shown that human activities have a profound impact on the atmospheric environment. At the same time, the atmospheric environment affects human health in different ways [1,2,3,4,5,6]. Researchers have demonstrated that anthropogenic activities directly affect the composition of atmospheric pollutants: heavy oil generated V and Ni [3,6]; As, Cd, Mn, Cr, Pb and Se could be produced in coal combustion [6,7,8]; Fe, Mn, Pb and Zn were tracer elements in the iron and steel industry [6]; Zn was commonly used as an additive in engine oil and Cu was present in brake pads [1]. Compared to natural particulate matter, finer particulate matter produced by human activities attained more adverse effects on the human body [9,10]. These fine particulate matters were able to penetrate more easily into the lungs and enter the blood circulation system, resulting in respiratory, mutagenic diseases, and even increased mortality [1,11,12]. PM2.5 (the atmospheric particulate matter with an aerodynamic diameter not greater than 2.5 μm), can not only affect water environment and crop quality through dry and wet deposition, but also threaten human health through respiration [13,14]. Metal elements took up a lower proportion of PM2.5, but possessed higher health risks in terms of human health and regional ecosystems because of their non-degradability [15,16].
In recent decades, the Chinese government and researchers have paid great attention to the study of environmental pollution [13,17,18]. Ten measures to prevent and control air pollution were put forward to ensure the completion of the “Action Plan for Air Pollution Prevention and Control” (APAPPC) issued in 2013. Under the influence of the APAPPC, the concentration of PM2.5 in BTH and the Yangtze River Delta decreased by 39.6% and 34.3%, respectively, in 2017 [19]. Existing research and policies focus on urban areas [5,20,21,22,23,24], but less research has been conducted on regional and background areas.
North China is one of the most important regions in northern China, with approximately 170 million people. North China has not only significantly different climatic characteristics and complex topographic features, but also abundant agricultural, coal, oil and mineral resources and convenient transportation. Unfortunately, the atmospheric environment in North China was not positive [25]. In order to distinguish the interaction between human activities and the atmospheric environment in North China, we discuss the influence of human activities on a regional atmospheric research station by comparing the variations and source apportionment of the 16 elements in regional and background atmospheric research stations, simultaneously. We screened the elements with the greatest impact on human health through a health risk assessment, hoping to reduce the amount of harmful elements by controlling human activities.

2. Data and Methods

2.1. Station Description

In our study, the Atmospheric Comprehensive Observation and Experiment Station (Xianghe Station, XH) and the Atmospheric Background Monitoring Network Observatory (Xinglong Station, XL) established by the Institute of Atmospheric Physics of the Chinese Academy of Sciences are selected as the research stations. XH is located in Daluotun Village, Shuyang Town, Xianghe County, Hebei Province (39°47′54″ N, 116°57′28″ E). It was created in 1973 and used as one of the solar earth space environment observation network stations of the Chinese Academy of Sciences. XL is seated on the top of Lianzhai Mountain, Xinglong County, Hebei Province (40°24′ N, 117°30′ E); it is surrounded by mountains and almost free of human activity. XL is one of the atmospheric background monitoring network observation stations of the Chinese Academy of Sciences. The exact location of the stations is shown in Figure 1.
The sampling periods in this study include the spring (from 16 March to 15 April), summer (from 20 June to 20 July), autumn (from 15 September to 14 October) and winter (from 15 December to 13 January of the following year) of 2017. Sampling, monitoring and details of the data quality control can be found in previous work [13,26].

2.2. Analytical Methods

2.2.1. Positive Matrix Factorization (PMF)

Paatero and Tapper first proposed a receptor model called PMF in 1994 [27]. The errors of chemical components in particulate matter were calculated by weight, and then the main pollution sources and their contribution rates were determined by the least square method. PMF is recommended as a standard source apportionment method by the US Environmental Protection Agency (EPA) [26]. More details on PMF are provided in previous studies [6,28,29].

2.2.2. Potential Source Contribution Factor (PSCF) and Concentration Weighted Trajectory (CWT) Analysis

The analyses of PSCF and CWT are based on the backward trajectory analysis of HYSPLIT, which was jointly developed by the Australian Meteorological Administration and the National Oceanic and Atmospheric Administration (NOAA) [30]. PSCF and CWT are performed in TrajStat with the Global Data Assimilation System meteorological data set [31,32,33].
PSCF can estimate the potential source regions by airflow trajectories [30,34,35], which is calculated with Equation (1):
P S C F = m i j / n i j
where nij is the total number of endpoints that fall into the grid cell (i, j) in the trajectory segment, mij is the total number of endpoints in nij and the concentration at the receptor site exceeds the threshold.
In order to calculate accurately, an arbitrary weight function (Wij) is multiplied to reflect the uncertainty of the median value [31,33,35,36]:
W i j = 1.0 ; n i j > 3 n a v e ; 0.7 ; 3 n a v e > n i j > 1.5 n a v e 0.4 ; 1.5 n a v e > n i j > n a v e 0.2 ; n a v e > n i j
where nij is the same as in Equation (1) and nave is the average number of endpoints that fall into the grid cell.
PSCF reflects the density but cannot distinguish the pollutant level of potential source regions. Fortunately, CWT can solve these problems [2,31,34,36]. The weighted concentration can be expressed as follows:
C i j = ∑ h = 1 M C h × τ i j h ∑ h = 1 M τ i j h × W ( n i j )
where: Cij is the average weighted concentration of the grid, μg/m3; Ch is the concentration of pollutants when the trajectory h reaches the grid, μg/m3; M is the number of trajectories; and τijh is the residence time of the trajectory h in the grid (i, j), h. To reduce the uncertainty of Cij, CWT uses the same weighting factor as PSCF.
To clarify the impact of the transmission on the research sites, 48-h backward trajectories at an altitude of 500 m are calculated at 00 and 12 UTC. The grid size of PSCF and CWT is 0.5° × 0.5° in the range of 30–45° N, 110–125° E. Since XL is a background station, 35 μg/m3 is taken as the threshold of PM2.5 according to the Ambient Air Quality Standard (GB3095-2012).

2.2.3. Health Risk Assessment

A health risk assessment can assess the possibility and extent of environmental damage to human health [6]. The harmful substances in the environment might cause harm to human health through inhalation, ingestion and skin contact [13,20]. According to the classification of the Integrated Risk Information System (IRIS) and the International Agency for Research on Cancer (IARC), the harmful elements are divided into carcinogenic elements (Cr, Ni, As and Cd) and non-carcinogenic elements (Cr, Mn, Ni, Cu, Zn, As, Cd and Pb) [29,37,38], which can be evaluated by incremental lifetime cancer risk (ILCR) and hazard quotient (HQ), respectively.
The exposure doses of the carcinogens (the life average daily dose (LADD, mg/(kg × d)) and non-carcinogens (the average daily dose, (ADD, mg/(kg × d)) were expressed as follows:
A D D ( L A D D ) = ( C × I R × E D × E F ) / ( B W × A T )
where C is the mass concentration of the pollutant, mg/m3; IR is the respiratory rate, m3/d; ED is the duration of exposure, d; EF is the exposure frequency, d/a; BW is the weight of the people, kg; and AT is the average exposure time, d. AT is 70 × 365 and AD × 365, respectively, in the calculation of carcinogenic risk and non-carcinogenic risk [3,13,38].
The cancer risk and non-carcinogenic risk were calculated with Equations (5) and (6):
I L C R = L A D D × S F
H Q = A D D / R f D
where SF is the slope factor of harmful elements, (mg/(kg × d))−1 and RfD represents the reference dose at different exposure routes, mg/(kg × d).
The acceptable cancer risk set by EPA is 1 × 10−6–1 × 10−4. A HQ value higher than 1 indicates a non-carcinogenic risk to people [13,29,38].

3. Results and Discussion

3.1. Temporal and Spatial Characteristics of PM2.5 and Elements

As can be seen from Figure 2a–d, the variation trends of PM2.5 in XL and XH were basically the same during 2017, but the annual average concentration of PM2.5 in XH was 54.9 μg/m3, which was significantly higher than the 35.6 μg/m3 in XL. In terms of seasonal variation, PM2.5 was highest in the spring and lowest in the autumn. In the spring of 2017, the concentration of PM2.5 in XH and XL was 65.6 μg/m3 and 42.3 μg/m3, respectively, which was 1.5 and 1.6 times higher than the concentration of autumn. It should be noted that the concentration of PM2.5 in XH and XL was 57.2 μg/m3 and 37.5 μg/m3 in winter, respectively, slightly higher than the concentration in summer (53.7 μg/m3 and 37.3 μg/m3), which might be mainly due to the establishment of the coal ban area in BTH. This was consistent with the results of existing studies in the coal ban area [31,39].
As shown in Figure 2e–h, the trend of total concentration in XL was similar to XH in 2017. Slightly different from the characteristics of PM2.5, the total concentration of elements in XH was 4744 ng/m3, which was significantly higher than that in XL (1717 ng/m3), especially in spring. XH had the highest concentration of total elements in spring with 8668 ng/m3, which was much higher than the 4690 ng/m3 in winter, 3856 ng/m3 in autumn and 2188 ng/m3 in summer. XL attained a similar variation pattern of XH, but the total concentration in spring was only 2928 ng/m3, which was about one third of that in XH. This was mainly because XH was situated in a rural area, which was greatly affected by anthropogenic activities, such as spring plowing and staggered production during the non-heating season, while XL was located in the mountains and was basically unaffected by human activities and the high vegetation cover was conducive to the reduction of particulate matter. Different from the seasonal variation of PM2.5, the concentration of total elements was much lower in summer and autumn. A possible reason was the higher boundary layer height and stronger scouring of rainwater and the viscosity of vegetation in summer and autumn, all of which were beneficial to the reduction of the concentration of elements, especially for the crustal elements.

3.2. Characteristics of the Elements

(1) Seasonal variation of the elements
Figure 3 illustrates that although the concentrations of different elements vary widely, they share similar seasonal characteristics. The concentrations of Ca, Mg, Al and Fe were approximately 102–103 ng/m3, Zn and Pb were generally approximately 101–102 ng/m3, but the concentrations of V, Cr, Ni and Cd were below 10 ng/m3. In terms of seasonal variations, most of the elements appeared to have the highest concentration in spring, followed by autumn and winter, and the lowest concentration of elements occurred in summer. The slight difference was that the concentration of most elements in XH was higher in winter, while most elements in XL were higher in autumn. This was mainly because XH had more pollutants released by human activities and poorer diffusion conditions of pollutants in winter. Meanwhile, XL was free from human activities and the concentration of pollution was mainly from transmission; compared to autumn, XL had fewer potential source regions in winter, which can be seen in Section 3.4.
In the spring of 2017, the concentrations of crustal elements such as Ca, Fe, Al, Mg and Mn in XH were 1942 ng/m3, 1677 ng/m3, 1660 ng/m3, 753 ng/m3 and 77 ng/m3, respectively, which were 1.81–2.08 times higher than those in winter, and much higher than those in other seasons. Compared to other seasons, although the concentration of crustal elements in XL was highest in spring, it was not as obvious as that in XH. The possible reason was that in addition to transmission, XH was influenced by local agricultural activities dust, road dust and other anthropogenic activities. Different from the crustal elements, the high concentration of heavy elements in spring was mainly influenced by activities such as coal consumption, oil consumption and off-peak production during the non-heating seasons [20].
(2) Daytime and nighttime variations in the elements
As illustrated in Figure 4, the daytime and nighttime concentrations of all elements in XH were higher than those in XL. The crustal elements at XH, such as Ca, Fe, Al, Mg, etc., attained diurnal concentrations of 1151 ng/m3, 972 ng/m3, 962 ng/m3 and 455 ng/m3, respectively, which were significantly higher than the nighttime concentrations of 872 ng/m3, 802 ng/m3, 782 ng/m3 and 352 ng/m3. This might be mainly due to the strength of anthropogenic activities in XH during the daytime, which increased the amount of agricultural dust and road dust, therefore the crustal elements increased significantly. With the weakening of anthropogenic activities at night, the crustal elements decreased significantly. The crustal elements in XL mainly came from the transmission of pollution, which led to completely different variation characteristics. With the decrease of boundary layer height during the nighttime, the concentration of crustal elements in XL increases significantly. Other polluting elements in XH and XL, such as Pb, Cu, As, Se, Cr, Ni, V, Cd, etc., were characterized by decreasing during the day and increasing during the night. The variation characteristics of these elements were similar to those of PM2.5, which were mainly affected by the reduction of the height of the boundary layer during the night.

3.3. Source Apportionment with the PMF Model

As a reliable method to identify the source of pollutants, PMF has been widely used in domestic and international research [13,28,29,40]. In our study, the source apportionment of 16 elements from XH and XL was analyzed in PMF 5.0, and the analytical results are shown in Figure 5.
Figure 5 shows that Cu, Zn, Pb and Cd attained the highest contribution to factor 1. Zn is often used as an additive in engine lubricants, while Cu is used in brake wear releases [1,36,41,42]. Cd is an important element occurring in lubricants and tires [5,43], while Pb and Zn appear in automobile exhaust fumes [5,29,36,40,43,44]. Tires and brake systems can produce large amounts of debris particles containing Zn, Cd, Pb and Cu [8,45]. Therefore, we classify factor 1 as the traffic source.
Mg, Al, Ca and Fe contributed the highest proportion to factor 2; they were the most common crustal elements [28,29,40,44,46]. Factor 2 was considered as soil dust. The soil dust may come from agricultural activities or the re-suspended dust due to traffic or construction activities.
In terms of factor 3, V, Cr and Ni contributed the most. V and Ni are typical markers of oil combustion [28,44,46]. Some researchers have found that the burning of oil released particles containing Cr, V and Ni [8,44]. Therefore, Factor 3 was identified as oil combustion.
As and Se obtained the highest contribution to factor 4, followed by Pb and Cd. As and Se are important inorganic tracers for coal combustion [5,8,28,40,44]. Since leaded gasoline was banned in China in 2000, coal combustion has become an important source of Pb [7,29,44]. We therefore categorized factor 4 as coal combustion.
The contributions of other elements to factor 5 were uniform except for Cu and Pb in XH and Na and K in XL. The possible reason is that XL is a background station, so the high contribution of K might be affected by biomass combustion. There is no large-scale industry near XH, so we classified factor 5 as others.
To analyze the changes in sources across different seasons, we calculated the proportions of different sources in the four seasons of 2017 (Figure 6). A comparative analysis showed that the proportion of soil dust was the highest in North China, accounting for 44.4–56.7% annually, with the highest proportion occurring in spring and the lowest in summer. The proportion of soil dust in the spring of XH was up to 68.2%, which was significantly higher than that in other seasons, which was largely because of agricultural activity, road dust and other activities around the station. Compared to XH, the proportion of soil dust in XL did not change dramatically, accounting for 35.3–50.3% in different seasons. Because of the high vegetation coverage and low human activity, XL obtained a much lower soil dust proportion than XH. Affected by the geographical location and traffic conditions, the traffic source in XH accounted for 18.3%, but only 10.9% in XL. Although the proportions of coal combustion and oil combustion in XL were higher than those in XH, the actual emissions in XL were lower than those in XH due to the lower total concentration of elements.

3.4. Potential Source Regions of PM2.5

PSCF and CWT analysis in TrajStat were employed to determine the influence of regional transmission in North China. In this study, 35 μg/m3 was taken as the threshold of PM2.5.
Figure 7a–h shows the potential source regions of the research sites in different seasons. The larger the PSCF value in the grid, the greater the probability of the influence of the grid area on the study site. As can be seen from these diagrams, the potential source regions of XH and XL did not differ much in the same season. The potential source regions in spring were mainly in the center of BTH. These areas transferred to southern BTH, northeastern Henan and western Shandong in summer. In the autumn and winter of 2017, the potential source regions mainly existed in the east and north of BTH, respectively. PSCFs display the probability of the contamination trajectory occurring in the grid directly, but cannot reflect the degree of pollution, so it is difficult to distinguish the potential source regions of high pollutants. To make up for that limitation, we quantitatively reflected the pollution level of different trajectories by determining the average weight concentration of each grid through CWT [2,31,33,36].
Figure 7i–p reflects the areas of high pollutants in different seasons. In the spring and winter of 2017, the weighted concentration of PM2.5 was greater than 50 μg/m3 in Beijing, Tianjin and the center of Hebei, which was significantly higher than that of the summer. This was mainly due to the slower movement of air masses in spring and winter during the sampling period. As the pollution air masses stayed in these areas for a long time, the weight concentration of PM2.5 increased. Conversely, rapidly moving air masses during the summer reduced the weight concentration of PM2.5 in the study sites.

3.5. Health Risk Assessment

The exposure parameters in the health risk assessment were affected by region, age, gender and activity intensity level (such as running and walking). The study of the respiratory rate in the exposure parameters showed that the gap between Chinese and American residents was 2.6–30.9% [32]. The specific parameters of the health risk assessment are shown in Table 1.
The results of the health risk assessment showed that the order of carcinogenic risk of the elements was Cr > As > Cd > Ni, in which the ILCR of Cr and As were greater than 1 × 10−6, while the ILCR of Ni was less than 1 × 10−6. This indicated that Cr and As posed a certain carcinogenic risk to human body, while Ni did not. The non-carcinogenic risk of elements was in the order of Mn > Cr > Zn > As, Pb > Cd > Cu > Ni. Although the HQ of Mn was below 1, its long-term effects cannot be ignored. In terms of different populations, the carcinogenic risk was shown as men > women > children, and the non-carcinogenic risk was shown as children > men > women. That is, the carcinogenic risk to adults was higher than the risk to children, while the non-carcinogenic risks were the opposite. Both the carcinogenic and non-carcinogenic risks were higher in men than women, which was consistent with the results of previous studies [13].
The comprehensive analysis of the health risk assessment of men and the source apportionment of PMF showed that although the proportion of soil dust in North China was the highest, the carcinogenic and non-carcinogenic risks caused by soil dust were small. In contrast, the risks caused by traffic were the largest (Figure 8). Long-term non-carcinogenic risks and possible carcinogenic risks need to be brought to the attention of the relevant authorities. Due to the data limitations, the elements in 2017 cannot fully demonstrate the source apportionment of PM2.5 and the cumulative health risks of these elements; we hope to conduct more in-depth research in future work.

4. Conclusions

The comparison of PM2.5 and 16 elements from regional and background atmospheric research stations in 2017 proved that human activities in North China have a great impact on this region. The concentrations of crustal elements increased with anthropogenic activities during the daytime and decreased during the nighttime in the regional station, but the characteristics of these elements in the background station were the opposite. Under the influence of anthropogenic activities, the crustal elements in the regional station gained the highest concentration in spring, accounting for 68.2% of the total element concentration, which was much higher than that of the background station. The study of the potential source regions found that the greatest impact on the research areas was from the center of BTH, followed by southern BTH and parts of Shandong and Henan, where anthropogenic activities were abundant. The comprehensive analysis of the health risk assessment and source apportionment indicated that the elements with a carcinogenic risk to humans mainly come from traffic fumes, which should provoke the attention of the relevant authorities.

Author Contributions

L.Y., R.S. and W.Z. designed the research. L.Y., W.Z. and Y.G. provided experimental assistance. L.Y., Y.G. and R.S. analyzed the data. L.Y. wrote and edited the manuscript. R.S. and W.Z. corrected the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Gansu Provincial Special Fund Project for Guiding Scientific and Technological Innovation and Development (2019ZX-06).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data sharing not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Distribution of regional research station and background research station of North China. The yellow and light blue dots represent regional station (XiangHe, XH) and background station (XingLong, XL), respectively. The red five-pointed star represents Beijing, the capital of China.
Figure 1. Distribution of regional research station and background research station of North China. The yellow and light blue dots represent regional station (XiangHe, XH) and background station (XingLong, XL), respectively. The red five-pointed star represents Beijing, the capital of China.
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Figure 2. (a–h) represent temporal and spatial variations of PM2.5 and the total concentration of 16 elements in North China during 2017. The blue and red dotted lines in (a–d) represent the normalized concentrations of PM2.5 with 35 μg/m3 and 75 μg/m3, respectively.
Figure 2. (a–h) represent temporal and spatial variations of PM2.5 and the total concentration of 16 elements in North China during 2017. The blue and red dotted lines in (a–d) represent the normalized concentrations of PM2.5 with 35 μg/m3 and 75 μg/m3, respectively.
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Figure 3. Seasonal variation of 16 elements at the regional research station and background research station. (a,c) use the left ordinate, while (b,d) use the right ordinate.
Figure 3. Seasonal variation of 16 elements at the regional research station and background research station. (a,c) use the left ordinate, while (b,d) use the right ordinate.
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Figure 4. Daytime and nighttime variations in 16 elements during 2017. (a) uses the left ordinate and (b) uses the right ordinate. D and N are short for daytime and nighttime, respectively.
Figure 4. Daytime and nighttime variations in 16 elements during 2017. (a) uses the left ordinate and (b) uses the right ordinate. D and N are short for daytime and nighttime, respectively.
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Figure 5. The source apportionment of 16 elements in XH and XL.
Figure 5. The source apportionment of 16 elements in XH and XL.
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Figure 6. The ratios of the different sources in XH (a) and XL (b) during the different seasons of 2017.
Figure 6. The ratios of the different sources in XH (a) and XL (b) during the different seasons of 2017.
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Figure 7. PSCF and CWT analysis of XH and XL in different seasons. (a–h) display the probability of the occurrence of contamination, (i–p) show the weighted concentration of PM2.5 in µg/m3.
Figure 7. PSCF and CWT analysis of XH and XL in different seasons. (a–h) display the probability of the occurrence of contamination, (i–p) show the weighted concentration of PM2.5 in µg/m3.
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Figure 8. Health risk assessment from different sources. (a,b) represent the carcinogenic and non-carcinogenic risks of different sources in XH, respectively, (c,d) represent the risks in XL corresponding to XH. The red and black dots in (a,c) represent values of 1 × 10−4 and 1 × 10−6 for ILCR, respectively. The red dots in (b,d) represent HQ = 1.
Figure 8. Health risk assessment from different sources. (a,b) represent the carcinogenic and non-carcinogenic risks of different sources in XH, respectively, (c,d) represent the risks in XL corresponding to XH. The red and black dots in (a,c) represent values of 1 × 10−4 and 1 × 10−6 for ILCR, respectively. The red dots in (b,d) represent HQ = 1.
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Table
Table 1. Exposure parameters in the health risk assessment of Chinese residents.
Table 1. Exposure parameters in the health risk assessment of Chinese residents.
ADD/LADD [mg/(kg × d)] IR (m3/d)EF (d/a)ED (d)BW (kg)
MAN19.02350.0030.0062.70
WOMAN14.17350.0030.0054.40
CHILD5.00350.006.0015.00
Note: IR is the respiratory rate; EF is the exposure frequency; ED is the duration of exposure; BW is the weight of the person. EF (d/a) and ED (d) were determined according to the Integrated Risk Information System (IRIS).
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Yao, L.; Si, R.; Zhang, W.; Guo, Y. The Interaction between Anthropogenic Activities and Atmospheric Environment in North China. Sustainability 2023, 15, 4636. https://doi.org/10.3390/su15054636

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Yao L, Si R, Zhang W, Guo Y. The Interaction between Anthropogenic Activities and Atmospheric Environment in North China. Sustainability. 2023; 15(5):4636. https://doi.org/10.3390/su15054636

Chicago/Turabian Style

Yao, Lanlan, Ruirui Si, Wenyu Zhang, and Yanling Guo. 2023. "The Interaction between Anthropogenic Activities and Atmospheric Environment in North China" Sustainability 15, no. 5: 4636. https://doi.org/10.3390/su15054636

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