Organophosphorus Pesticide Exposure at 17 Weeks’ Gestation and Odds of Offspring Attention-Deficit/Hyperactivity Disorder Diagnosis in the Norwegian Mother, Father, and Child Cohort Study
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Are common environmental problems which can easily affect human health. Chengdu is a major central city in Western China, and there is little research on the regional emissions and health effects of air pollution in Chengdu. According to the Multi-resolution Emissions Inventory of the Chinese Model, 2017 (MEIC v1.3), this study compiled the air pollutant emission inventory of Chengdu. The results show that the pollutant emission of Chengdu is generally higher in winter than in summer. The southeast area of Chengdu is the key area where emissions of residential and industrial sectors are dominant. Through air quality simulation with a Weather Research and Forecasting model, coupled with the Community Multiscale Air Quality (WRF-CMAQ), the health effects of PM
Pollution accounted for 328 deaths in the same period. There were 276 deaths in rural areas and 413 in urban areas. In January and July 2017, the health economic loss caused by PM
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Air pollution is one of the universal environmental problems in the world. The environmental pollution caused by the concentration of fine particulate matter (PM
) exceeding the air quality standard has attracted people’s attention [1, 2, 3, 4, 5]. Fine particulate matter in the air with small particle size can be suspended in the air for a long time; its composition is complex, containing sulfate, nitrate and other chemical components, as well as toxic trace elements [6, 7]. PM
Has negative impacts on health by triggering inflammatory, mutagenic/genotoxic, and intracellular oxidative stress responses [8]. Atmospheric ozone pollution is due to anthropogenic emissions of pollutants of many precursors including VOCs and NO
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In order to clarify the impact of air pollution on human health, many scholars have conducted research from the perspective of health exposure risk [12, 13, 14, 15, 16]. Hu et al. [17] estimated the PM
Premature mortality amount was about 3.15 million/year in 2010 globally, and China is the leading country, with about 1.33 million/year. Ding, D et al. [18] indicated that the total mortality of PM
In China in 2013 was estimated to be 1.389 million (95%CI: 10.05 million, 1.631 million), and in 2017 was estimated to be 1.102 million (95%CI: 0.775 million, 1.337 million), the health burden of PM
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Pollution in China is much higher than the other developed countries. Studies show that respiratory mortality and non-accidental mortality increased by 1.24% (95%CI: 0.29%, 2.22%) and 0.36% (95%CI: 0.10%, 0.63%) with the increase of PM
, respiratory mortality and non-accidental mortality increased by 2.22% (95%CI: 0.56%, 3.90%) and 0.05% (95%CI: 0.42%, 0.53%), respectively [19]. In 2030, without controlling policies, PM
In addition to being a significant metropolis in the center of western China, Chengdu also serves as the business, financial, and transportation hub for southwest China. There is, however, limited research on the emission of pollutants and their consequences on health in various locations within Chengdu.
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In this paper, a Weather Research and Forecasting model coupled with the Community Multiscale Air Quality (WRF-CMAQ) is used to simulate the air quality of Chengdu in January and July 2017, to consummate the monitoring value and obtain complete air quality data. Based on this, the emission of pollutants in winter and summer and the health and economic losses caused by PM
The WRF-CMAQ model is used to simulate the air quality of Chengdu in January (winter) and July (summer) in 2017. The atmospheric pollutants concentration data of environmental quality automatic monitoring station of Chengdu from China’s environmental monitoring station of national urban air quality real-time publishing platform (http://beijingair.sinaapp.com/) (accessed on 5 July 2022) and the meteorological data of Shuangliu District (SL) meteorological station from China meteorological data service center (http://data.cma.cn/) (accessed on 10 July 2022) were collected for WRF-CMAQ model simulation test and the analysis of research. The Community Multiscale Air Quality model (CMAQ v5.3.2) (https://www.cmascenter.org/cmaq/) (accessed on 3 July 2022) model is used in this study.
Figure 1 shows the nested domain used in this study. Domain 1 covers most of western China, including Sichuan Province, Guizhou Province, Chongqing Municipality, Yunnan Province, Tibet Autonomous Region, Qinghai Province, Gansu Province, Shaanxi Province, with a mesh resolution of 27 km × 27 km; Domain 2 covers the Sichuan Province with a grid resolution of 9 km × 9 km and Domain 3 covers Chengdu with a grid resolution of 3 km × 3 km. The input meteorological data used for the model were generated by Weather Research and Forecasting Model (WRF 3.9.1), according to the National Centers for Environmental Prediction Final (NCEP FNL). global tropospheric analysis business model dataset with a spatial resolution of 1° × 1° and a temporal resolution of 6 h. The Multi-resolution Emission Inventory of China (MEIC v1.3, 0.25° × 0.25°) of 2017 (http://www.meicmodel.org/) (accessed on 5 July 2022) [21, 22] was used for the pollutant emissions inventory. The inventory contains 10 major air pollutants and CO
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) from five sectors: industry, power, residential, transportation and agriculture, with VOCs allocated according to the Carbon Bond-06 mechanism. MEIC is an emission inventory model framework based on bottom-up technology. The study used Inventory Spatial Allocate Tool (ISAT) to redistribute species on the temporal and spatial scales of Multi-resolution Emission Inventory of China of 2017 based on population and facilities, and established a high-resolution emission inventory (3 km × 3 km) of Chengdu, which was used as input to the CMAQ model. To eliminate the influence of initial conditions, the WRF started 7 days in advance.
The environmental population data, mortality data and socio-economic data were obtained from the 2017 Chengdu Statistical Bulletin of National Economic and Social Development. In 2017, Chengdu had a permanent population of 14.353 million, including 8.512 million urban and 5.841 million rural residents. The human health burden of short-term exposure to atmospheric pollutants can be calculated by the following formula [23, 24]:
Is the total mortality caused by air pollutants exceeding a certain limit value, n is the number of days of environmental monitoring, Y
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Is the daily relative risk associated with short-term exposure to air pollutants, β is the empirical coefficient of concentration and health response obtained based on epidemiological investigation. In this study, the β% value is 0.38 (95%CI: 0.31–0.45) for every 10 μg·m
Concentration, the β% value is 0.40 (95%CI: 0.30–0.50) [26]; X is the concentration of air pollutants. In this study, the daily average concentration of PM
Statistical vital value (VSL) is commonly used to monetize the risk of premature death caused by air pollutants. This method mainly includes willingness to pay (WTP) and human capital (HC) methods [27]. Peng, et al. [28] investigated the average willingness to pay and vital statistical value of reducing air pollution health risks in Chengdu-Chongqing region in 2018 based on the single boundary dichotomic conditional value method, which is very consistent with this study. Economic losses from environmental health impacts are calculated as follows [29, 30]:
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VSL is the vital statistical value of different years, and the vital statistical value of Chengdu in 2018 is 4.02 million yuan. I is the disposable income per capita of Chengdu in different years, which is 20, 300 yuan in 2017 and 22, 100 yuan in 2018. E
The simulation of meteorological conditions and pollution levels of key pollutants by the model was verified (Figure 2 and Figure 3). The temperature at 2 m (T2) provided by WRF simulation performed well in January and July, while the wind speed at 10 m (U10) performed poorly, which may be due to the unique basin topography of Sichuan and related to the parameterization scheme. However, the U10 simulation average can reach 87~98% of the observed value. The overall simulated variation in PM
In Chengdu are in the low concentration range in summer and winter, respectively, and are underestimated, which is consistent with the study of Hu et al. [31]. The NMB of PM
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Is about ±0.50 (Table 1 and Table 2), which also indicates that the change trend of pollutants is well understood [32]. The relevant meteorological parameters and pollutant
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