Association of PM2.5 from agriculture sources and acute myocardial infarction onset: results from 2015 to 2018 in China : Cardiology Plus

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Original Article

Association of PM2.5 from agriculture sources and acute myocardial infarction onset: results from 2015 to 2018 in China

Du, Peng1; Lu, Kailai1; Zhang, Can1; Liu, Ting2; Hu, Jianlin2; Li, Tiantian1,*

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Cardiology Plus ():10.1097/CP9.0000000000000048, May 19, 2023. | DOI: 10.1097/CP9.0000000000000048
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Myocardial infarction is an acute and multifactorial manifestation of coronary artery disease, and its prevalence has increased globally in the past few decades[1,2]. As a principal type, acute myocardial infarction (AMI) deserves increasing attention considering its various fatal consequences (e.g., heart failure, sudden cardiac death)[3,4]. Fine particulate matter (PM2.5) is regarded as a risk factor for AMI-associated mortality[4,5] and hospitalizations[6–9]. PM2.5 is a complex mixture of numerous compounds derived from various emission sources, it has been associated with adverse health effects and increases the risk of adverse cardiovascular outcomes[9–12]. Ammonia derived from agricultural activities is an air pollutant, and as a key precursor for PM2.5, it contributes greatly to PM2.5 formation through diverse chemical pathways[13–15]. Agricultural activities have become the primary source of PM2.5 in many European and Asian countries[16,17].

Epidemiological studies have focused on the potential health risks due to PM2.5 exposure in agricultural areas or communities[18–20]. However, studies on the health effects caused by PM2.5 from agricultural sources are limited, and findings from such studies are controversial. For instance, Huang et al. reported that exposure to PM2.5 from agricultural sources was significantly associated with emergency department visits for acute upper respiratory tract infections in the US State of Georgia, determined using a multi-source model (all 12 sources included), and they reported insignificant results with the single-source model (only agricultural source was included)[21]. As reported by the Dutch Environmental Longitudinal Study, long-term exposure to primary PM2.5 sources, except agricultural emissions, was significantly associated with mortality[22]. Some studies have assessed source contributions from agricultural emissions and the consequent impact on premature mortality burden[16,23]. However, the health risks associated with exposure to PM2.5 from agricultural sources are equal to those associated with other sources in terms of pollutant toxicity, rather than the evaluated source-specific estimation. Thus far, no study has assessed the association between exposure to PM2.5 from agricultural sources and AMI onset.

Recently, China has shown a remarkable improvement in air quality by implementing various emission control measures. Agricultural practices contribute to 88% of the total ammonia emission in China[14,24]. Control of ammonia from agricultural emission has attracted great attention from the Chinese government, which led to the launch of a national research program on the cause and improvement of PM2.5 pollution, supported by Chinese Premier Funding in 2017, and the formulation of follow-up action plans[14]. However, the prevalence and mortality rates associated with AMI have shown an upturn in the past 20 years, which has become a challenging issue to tackle in China[25]. These findings indicate an exceptional advantage in exposure to a wide range of PM2.5 concentrations from agricultural sources and a large number of patients with AMI onset in China. Therefore, in the present study, we investigated the increased risk of AMI onset after short-term exposure to PM2.5 from agricultural sources to provide epidemiologic evidence of the associated health effects. These findings are expected to be useful for formulating pollution control strategies to protect the susceptible population.


Study population

Medical records were extracted from the China Cardiovascular Association (CCA) Database-Chest Pain Center, and the data of 355,815 patients with AMI onset from 1,653 hospitals (Figure 1) were collected for the period 2015 to 2018, covering 1,155 counties from 31 provinces, municipalities, or municipal districts in China. The CCA Database-Chest Pain Center was constructed based on the largest domestic certificated network of chest pain centers in China[26]. Data on patients’ age, sex, AMI onset date, diagnosis, and treatment were collected from the records. For this study, AMI codes according to the 10th Revision of the International Classification of Diseases (ICD-10; code: I21) were relevant. AMI cases were classified into ST-elevation myocardial infarction (STEMI; codes: I21.0-I21.3) and non-STEMI (NSTEMI; code I21.403) according to patients’ electrocardiography results. This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and approved by the Ethical Committee of the National Institute of Environmental Health, Chinese Center for Disease Control and Prevention (NO. 202102; date: February 18, 2021). Due to the retrospective nature of the study, informed consent was waived by the Ethical Committee of the National Institute of Environmental Health, Chinese Center for Disease Control and Prevention.

Figure 1.:
Location of the 1,653 hospitals registered in the network of China Chest Pain Centers AMI: acute myocardial infarction.

Environmental and meteorological data

Daily concentrations of PM2.5 from agricultural sources for the period 2015 to 2018 were generated using the source-oriented Community Multiscale Air Quality (CMAQ) modeling system (version 5.2), which has been widely used in epidemiological studies[27–29]. Source appointment of the CMAQ model was performed by considering multiple emission inventories, and both model description and model performance have been previously reported[23,30,31]. Coarse simulation results were obtained at a resolution of 36 km × 36 km, after which it was downscaled to 0.1° × 0.1° (approximately 10 km × 10 km) by introducing the validated Tracking Air Pollution (TAP) in China dataset and by using the inverse distance weighting method. The downscaling procedure and exposure assessment have been documented previously[12]. We also collected meteorological data from the re-analysis database of the European Centre for Medium-Range Weather Forecasts at a resolution of 0.1° × 0.1° (approximately 10 km × 10 km), and the collected data were converted into daily temperature and relative humidity at the county level[12,32]. Considering the missing data of patients’ residential address, exposure assessments of AMI patients in this analysis were matched with the data available in the patients’ reporting hospital at the county level.

Statistical analysis

Study design

We proposed a time-stratified case-crossover study to investigate the association between exposure to PM2.5 from agricultural sources and AMI onset. The self-controlled feature of this method makes it advantageous to control the long-term trend, seasonality, and individual-level covariates (e.g., age, sex, smoking status, and alcohol drinking status)[33,34]. This method has been extensively applied to estimate the health risks of acute exposure to ambient air pollution[34,35]. In this study, we defined each individual with AMI onset as a case on AMI onset day and recognized the same weekday in the same month of the year as the control days for each AMI case. Generally, there were 3 or 4 control days corresponding to each case day.

Statistical model

We used conditional logistic regression models to determine the risk of AMI onset associated with short-term exposure to PM2.5 from agricultural sources. In the regression model, we included PM2.5 from agricultural sources and adjusted for daily mean temperature as a natural cubic spline function using 3 degrees of freedom (df). We also controlled for relative humidity and public holiday, considering them as a linear covariate and a binary variable, respectively. Lag effects with a maximum of 3 days were also examined in this study, considering the single and moving average effects. Moreover, the same lag selection was considered for daily mean temperature, relative humidity, and PM2.5 from agricultural source simultaneously. As for the subtypes of AMI, STEMI and NSTEMI were assessed as health outcomes. We also performed additional subgroup analyses stratified by age (<65 and ≥65 years), sex (male and female), AMI onset season (cold and warm), region (north and south), and setting (rural and urban areas). The warm season spanned from April to September, and the cold season spanned from October to March next year. North and south regions were divided by the Qinling–Huaihe Line. Rural and urban areas were defined according to the population density of the area at the county level, that is, rural areas included counties with a population density below 500/km2, and urban areas included counties with a population density above 500/km2[36]. In subgroup analyses, differences between the subgroups were determined using the two-sample z-test method[4,37].

Sensitivity analysis

To determine the robustness of the results, we conducted sensitivity analyses for various parameters in this study. First, we selected different dfs for daily temperature and relative humidity. Models 2 to 5 were proposed to examine 4, 5, and 6 dfs and the linear type for temperature, and Models 6 to 9 were adjusted for meteorological indicators using the same dfs (i.e., 3, 4, 5, and 6), simultaneously. Second, we introduced ozone concentration as a parameter to investigate a two-pollutant model. Third, we used a multi-source model wherein five other sources of ammonia were introduced, namely, residential use, open-burning, biogenic source, initial and boundary conditions, and other sources. Sources from transportation, industry, and power generation were excluded owing to their high collinearity (Spearman partial correlation of >0.7). Fourth, we determined the health risks of exposure to PM2.5 using the coarse CMAQ simulation at a resolution of 36 km × 36 km.

The R software 4.0.2 (The R Foundation for Statistical Computing, Vienna, Austria) was used for the statistical analysis. Data of health risk estimation were expressed as odds ratio (OR) and its 95% confidence interval (95% CI) for each 10 μg/m3 increase in exposure. Moreover, a two-sided alpha level of 0.05 was considered to judge whether or not the analysis result showed a statistically significant difference.


Descriptive analysis

Among the 355,815 patients with AMI onset during 2015 to 2018, 67.1% had STEMI onset, 73.9% were male, 53.0% were aged below 65 years, 52.7% had AMI onset during the cold season, and 57.6% settled in northern regions of China (Table 1). Moreover, 75,314 cases of AMI onset were recorded in 622 hospitals in the rural area. On the days of AMI onsets, the mean exposure levels of total and agricultural source PM2.5 were 47.87 μg/m3 and 5.32 μg/m3, respectively, which were slightly greater than the levels recorded on control days. As for mean temperature and relative humidity, no discernible difference was noted between the groups (Table 2).

Table 1. - Demographic characteristics of the study population assessed during 2015–2018
Indicators n Percentage
AMI (ICD-10: I21) 355,815
 STEMI (I21.0–I21.3) 238,790 67.1
 NSTEMI (I21.4) 117,025 32.9
Case days 355,815
Control days 1,210,855
 Male 262,947 73.9
 Female 92,868 26.1
Age at onset, y
 <65 188,708 53.0
 65–74 94,537 26.6
 ≥75 72,570 20.4
 Warm (April to September) 168,400 47.3
 Cold (October to March next year) 187,415 52.7
 North 205,097 57.6
 South 150,718 42.4
 Rural 75,314 21.2
 Urban 280,501 78.8
Case days mean the dates of admission for AMI onsets, and control days refer to the matched dates to case days on the same weekday in the same month of the year.
AMI: acute myocardial infarction; ICD-10: International Classification of Diseases (10th revision); NSTEMI: non–ST-elevation myocardial infarction; STEMI: ST-elevation myocardial infarction.

Table 2. - PM2.5 exposure levels on case days and control days
Items n Mean SD Min Q25 Q50 Q75 Max IQR
Case days 355,815
 Total PM2.5, μg/m3 47.87 36.45 1.00 23.96 37.58 59.54 459.43 35.58
Agriculture source PM2.5, μg/m3 5.32 4.94 0.00 2.02 3.87 6.93 57.12 4.90
 Mean temperature, ℃ 14.19 11.22 -35.48 6.43 16.08 23.34 35.59 16.91
 Relative humidity, % 66.40 18.10 7.75 54.20 69.99 80.77 99.84 26.58
Control days 1,210,855
 Total PM2.5, μg/m3 47.32 36.19 1.00 23.66 37.07 58.81 459.43 35.15
Agriculture source PM2.5, μg/m3 5.26 4.91 0.00 2.00 3.81 6.85 58.17 4.85
 Mean temperature, ℃ 14.20 11.22 -35.48 6.51 16.09 23.36 37.64 16.85
 Relative humidity, % 66.46 18.14 5.01 54.20 70.08 80.89 99.92 26.69
Case days mean the dates of admission for AMI onsets, and control days refer to the matched dates to case days on the same weekday in the same month of the year.
IQR: interquartile range; Max: maximum; Min: minimum; PM2.5: particulate matter ≤2.5 μm in aerodynamic diameter; Q25: 25% quantile; Q50: 50% quantile; Q75: 75% quantile; SD: standard deviation.

Risk assessment

Estimations of AMI onsets concerning PM2.5 from agricultural sources during multiple single and moving average lags are presented in Figure 2. A downward tendency in ORs was observed with an increase in lags, particularly for the single lags, and the largest estimation occurred on the current day (i.e., lag 0). Lag 0 exposure to PM2.5 from agricultural sources was significantly associated with the onset of AMI and its subtypes. The ORs (95% CI) were 1.044 (1.033–1.055) for AMI onset, 1.049 (1.035–1.063) for STEMI, and 1.034 (1.015–1.054) for NSTEMI, for each 10 μg/m3 increase in exposure to agricultural source PM2.5.

Figure 2.:
Odds ratios of AMI onset concerning exposure to PM 2.5 from agricultural sources with a 10 μg/m 3 increase in exposure during multiple lags. Lag 0: the current day; lag 1: the previous day; lag 2: the day before lag 1; lag 3: the day before lag 2; lag 01: the 2-day moving average; lag 02: the 3-day moving average; lag 03: the 4-day moving average. 95% CI: 95% confidence interval; AMI: acute myocardial infarction; NSTEMI: non–ST-elevation myocardial infarction; OR: odds ratio; PM2.5: particulate matter ≤2.5 μm in aerodynamic diameter; STEMI: ST-elevation myocardial infarction.

Sensitivity analyses

In sensitivity analyses, only negligible change was observed when analyzing multiple dfs for meteorological indicators and the two-pollutant model with ozone concentration (Table 3), indicating the robustness of our model performance. In the multi-source model, the positive associations were relatively stable, and the association between exposure to PM2.5 from agricultural sources and NSTEMI onset became null. Additionally, positive associations were obtained using the coarse database at a resolution of 36 km × 36 km, although they decreased to some extent when compared with that in the main model, implying a possible biased effect without downscaling and adjustment.

Table 3. - Results (OR [95% CI]) of sensitivity analyses
Model 1 (main model) 1.044 (1.033–1.055) 1.049 (1.035–1.063) 1.034 (1.015–1.054)
Model 2 (df temp = 4, rh = linear) 1.045 (1.033–1.056) 1.050 (1.036–1.064) 1.035 (1.015–1.055)
Model 3 (df temp = 5, rh = linear) 1.045 (1.034–1.056) 1.050 (1.036–1.064) 1.035 (1.016–1.055)
Model 4 (df temp = 6, rh = linear) 1.045 (1.034–1.056) 1.049 (1.036–1.063) 1.036 (1.016–1.056)
Model 5 (temp = linear, rh = linear) 1.043 (1.032–1.054) 1.048 (1.034–1.062) 1.033 (1.014–1.052)
Model 6 (df temp = 3, dfrh = 3) 1.040 (1.029–1.052) 1.044 (1.030–1.058) 1.032 (1.012–1.052)
Model 7 (df temp = 4, df rh = 4) 1.041 (1.030–1.053) 1.045 (1.031–1.059) 1.033 (1.013–1.053)
Model 8 (df temp = 5, df rh = 5) 1.041 (1.030–1.053) 1.045 (1.031–1.059) 1.033 (1.013–1.053)
Model 9 (df temp = 6, df rh = 6) 1.041 (1.030–1.053) 1.045 (1.031–1.059) 1.034 (1.014–1.054)
Model 10 (controlling O3 from TAP database) 1.043 (1.031–1.054) 1.047 (1.033–1.061) 1.034 (1.014–1.054)
Model 11 (controlling other five sources) 1.030 (1.013–1.047) 1.036 (1.016–1.057) 1.018 (0.989–1.048)
Model 12 (using data from 36 km × 36 km grids) 1.039 (1.030–1.048) 1.039 (1.028–1.050) 1.038 (1.023–1.054)
Model 1: main model; Model 2: 4 dfs for temperature; Model 3: 5 dfs for temperature; Model 4: 6 dfs for temperature; Model 5: temperature as linear type included in model; Model 6: a natural cubic spline with 3 dfs for relative humidity; Model 7: 4 dfs for temperature and relative humidity; Model 8: 5 dfs for temperature and relative humidity; Model 9: 6 dfs for temperature and relative humidity; Model 10: examining two pollutants model including ozone (from TAP database); Model 11: fitting a multi-source model including other five sources; Model 12: using data from 36 × 36 km2 grids generating from the Community Multiscale Air Quality (CMAQ) model. linear means a linear covariate.
AMI: acute myocardial infarction; df: degree of freedom; dfrh: df for relative humidity; dftemp: df for temperature; NSTEMI: non-ST-elevation myocardial infarction; rh: relative humidity; STEMI: ST-elevation myocardial infarction; TAP: Tracking Air Pollution in China database.

Stratified analyses

Associations between exposure to PM2.5 from agricultural sources and AMI onset were relatively consistent across the subgroups examined (Figure 3). Females tended to have higher risk estimations attributed to agricultural source PM2.5 for AMI and NSTEMI onsets, although the difference was insignificant (P > 0.050). In age-stratified analysis, significantly greater associations were observed in patients aged above 65 years (P = 0.032) in the NSTEMI group. In region-stratified analysis, larger estimated risks were pronounced in southern China than in northern China, and significant differences were observed between the AMI and NSTEMI groups (P = 0.017 and 0.024). Meanwhile, inconsistent risk estimations were observed in season-stratified analysis, and the results simultaneously showed greater risks of STEMI and NSTEMI onset in the warm and cold seasons, respectively. Additionally, relatively higher associations were obtained for rural areas than for urban areas, with an insignificant difference.

Figure 3.:
Odds ratios of AMI onset concerning agricultural source PM 2.5 with a 10 μg/m 3 increase in exposure in the stratified analysis at current day (lag 0). AMI: acute myocardial infarction; NSTEMI: non–ST-elevation myocardial infarction; OR: odds ratio; PM2.5: particulate matter ≤2.5 μm in aerodynamic diameter; STEMI: ST-elevation myocardial infarction.


To the best of our knowledge, this research is the first nationwide case-crossover study to explore the association between exposure to PM2.5 from agricultural sources and AMI onset, based on exposure assessment from source-oriented and downscaled CMAQ and large-scale AMI onset records from the certificated collaborative healthcare network. We observed that short-term exposure to PM2.5 from agricultural sources was significantly associated with a 4.4% (3.3%–5.5%), 4.9% (3.5%–6.3%), and 3.4% (1.5%–5.4%) increase in the odds of AMI, STEMI, and NSTEMI onset for each 10 μg/m3 increase at lag 0 day. Meanwhile, higher risk estimations were pronounced in individuals aged above 65 years, who lived in southern China, and who resided in the rural area setting, and significant differences were observed mostly for NSTEMI onset.

Currently, no study has examined the risk of AMI onset due to PM2.5 from agricultural sources. The association between total PM2.5 and AMI-related hospitalization has been reported previously, and meta-analyses have reported that each 10 μg/m3 increment in total PM2.5 was associated with an approximately 2.0% to 2.5% higher AMI risk[1,38,39]. PM2.5 concentrations from agricultural sources calculated in the present study were higher than the reported health estimations of total PM2.5, which partly explains that ammonia from agricultural source is beneficial for the formation of secondary PM2.5[14]. Although we could not directly compare our results of agricultural source with those of other studies, the risk of STEMI onset tended to be greater than that of NSTEMI onset, consistent with the findings of previous studies of PM2.5 or its sources[7]. PM2.5 can also trigger plaque rupture and thrombus formation, both of which are characteristic of STEMI[7,40–42].

Our results of stratified analyses showed higher associations in patients aged above 65 years, in line with previous findings of AMI onset due to PM2.5 from agricultural sources and related sources[2,12,43]. This result may be attributed to the fragile cardiovascular system in elderly patients, which is easily susceptible to impairment by air pollutants[37]. Meanwhile, greater AMI onset risk estimations were obtained for individuals who were living in southern than in northern China, which may be attributed partially to the difference in agricultural patterns, ammonia emission levels, and meteorological factors in those regions. Some previous studies have discussed the difference in PM2.5 characteristics between rural and urban areas[18]. We also obtained slightly higher associations for rural areas, which may be partly explained by the relatively higher concentrations of PM2.5 from agricultural sources. Compared with STEMI onset, NSTEMI onsets showed significant differences between the groups, indicating the differences in the effects and potential mechanisms underlying different AMI subtypes after short-term exposure to PM2.5, coherent with the findings of a previous epidemiological study[7].

Notable strengths of the present study include the nationwide design and large sample size (i.e., 355,815 patients with AMI onsets). The records of all patients with AMI onset were collected from the certificated network of chest pain centers in China, wherein AMI was diagnosed by cardiologists according to standard guidelines, that is, based on clinical symptoms, medical history, electrocardiogram findings, and laboratory test results. Meanwhile, PM2.5 concentrations from agricultural sources were simulated based on the source-oriented CMAQ model, which included full coverage of all counties in mainland China. The initial resolution was further downscaled and adjusted to a relatively fine resolution (approximately 10 km × 10 km). The time-stratified case-crossover study design was key to determine the short-term exposure effects, owing to its unparalleled advantages in controlling individual confounders that did not change over time. The risk of the onset of AMI and its subtypes (STEMI and NSTEMI) was investigated simultaneously, which provided robust and comprehensive epidemiological evidence.

However, the study also had several limitations that need to be considered. First, the exposures were evaluated based on CMAQ model simulation, which potentially resulted in exposure misclassification. However, the CMAQ model has been extensively used in studies on health effects, particularly for studying PM2.5 constituents and their sources, owing to significantly less available data from the monitoring network[27–29,44,45]. Notably, we also used a downscaling and adjustment method to achieve a fine spatiotemporal resolution from 36 km × 36 km to 10 km × 10 km, which is to be acceptable when compared with that in other previous studies[27–29,46,47]. Second, exposure assessment of AMI cases was matched according to the hospital location (address) at the county level, rather than the location at which the onset occurred (e.g., residential or working places). We believed that such an assessment bias would not cause any drastic changes to the results, especially considering that the patient with AMI sought immediate medical attention at nearby hospitals to reduce the acute attacks and fatal consequences. Additionally, rural and urban areas were categorized according to population density, which would have introduced uncertainty in the subgroup analysis. Third, the medical records extracted from the CCA Database-Chest Pain Center included only records of hospital admissions, and there was no information about patients who did not reach the hospital or died before reaching the hospital[7,37]. Fourth, we examined only the health risks of exposure to PM2.5 from agricultural sources generated from the model simulation, but we did not investigate the risks of its specific constituents to further improve the robustness of our results.


This study is the first to report the association between exposure to PM2.5 from agricultural sources and AMI onset by integrating the sources-oriented CMAQ model and large-scale onset records from China Chest Pain Centers. Our findings indicate that short-term exposure to PM2.5 from agricultural sources would trigger AMI onset, which provides a better understanding of the health risk of PM2.5 exposure from agricultural source contributions. As revealed in this study, in the future, more attention should be paid to the reduction in PM2.5 concentrations in agricultural sectors (e.g., ammonia emission abatement by improving farming practices).


This work was funded by the National Natural Science Foundation of China (92143202 and 92043301) and the Open Fund of Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (KHK2204).


PD conducted statistical analyses and drafted the manuscript. KL contributed to adjustment and downscaling of CMAQ data. CZ assisted the visualization. TL and JH carried out the CMAQ simulations. TL designed analysis strategy and structure and modified the manuscript.


The authors declare that they have no financial conflict of interest with regard to the content of this manuscript.


The datasets generated during and/or analyzed during the current study are not publicly available due to its individual-level characteristics but are available from the corresponding author on reasonable request.


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Agriculture; Particulate matter; Myocardial infarction; Case-crossover

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