ABSTRACT: We use the ARIMA-GARCH model to estimate the shocks of COVID-19 on Chinese agricultural markets and utilize the ARDL-ECM model to estimate the integration relationships between Chinese and international agricultural markets which are based on the effective market hypothesis theory and daily trading data of US, UK, China and India. The main study findings showed that COVID-19 has a significant negative impact on agricultural product market, specifically, India suffered with the greatest negative impact, followed by the UK, the US and China. Further, this study results revealed that Chinese grain markets that are considered strategic places have not been separated from international markets, but there still exist segmentation phenomena among non-strategic agricultural markets.

RESUMO: Utilizamos o modelo ARIMA-GARCH para estimar os choques da COVID-19 nos mercados agrícolas chineses e utilizamos o modelo ARDL-ECM para estimar as relações de integração entre os mercados agrícolas chineses e internacionais que se baseiam na teoria da hipótese de mercado eficaz e nos dados comerciais diários dos EUA, Reino Unido, China e Índia. As principais conclusões do estudo mostram que a COVID-19 tem um impacto negativo significativo no mercado de produtos agrícolas, especificamente, a Índia sofreu o maior impacto negativo, seguida pelo Reino Unido, pelos EUA e pela China. Além disso, os resultados deste estudo revelam que os mercados chineses de cereais, considerados locais estratégicos, não foram separados dos mercados internacionais, mas ainda existem fenômenos de segmentação entre os mercados agrícolas não estratégicos.

ABSTRACT OBJECTIVE To evaluate the relationship between ambient air pollutants and chronic obstructive pulmonary disease in relatively low-polluted areas in China. METHODS Atmospheric pollutants levels and meteorological data were obtained from January 2016 to December 2020. The medical database including daily hospital admissions for chronic obstructive pulmonary disease (ICD10: J44) was derived from the First Affiliated Hospital of Gannan Medical University. The generalized additive model was used to analyze the percentage change with 95% confidence interval in daily hospital admissions for chronic obstructive pulmonary disease associated with a 10 µg/m3 increase in atmospheric pollutants levels. RESULTS In total, occurred 4,980 chronic obstructive pulmonary disease hospital admissions (not including emergency department visits) during 2016–2020. The mean concentrations of daily PM2.5, PM10, SO2, NO2, O3, and CO were 37.5 μg/m3, 60.1 μg/m3, 18.7 μg/m3, 23.5 μg/m3, 70.0 μg/m3, and 1.2 mg/m3 in Ganzhou. Each 10 µg/m3 increment of PM2.5, PM10, NO2, and O3 were significantly associated with 2.8% (95%CI: 1.0–4.7), 1.3% (95%CI: 0.3–2.4), 2.8% (95%CI: 0.4–5.4), and 1.5% (95%CI: 0.2–2.7) elevation in daily chronic obstructive pulmonary disease hospital admissions. The estimates of delayed effects of PM2.5, PM10, NO2, and O3 were observed at lag6, lag6, lag8, lag1, respectively. The health effects of particulate pollutants (PM2.5 and PM10) may be independent of other pollutants. The adverse effects of air pollutants were more evident in the warm season (May–Oct) than in the cold season (Nov–Apr). CONCLUSION Our study demonstrated that elevated concentrations of atmospheric pollutant (PM2.5, PM10, NO2, and O3), especially particulate pollutants, can be associated with increased daily count of hospital admissions for chronic obstructive pulmonary disease , which may promote further understanding of the potential hazards of relatively low levels of air pollution on chronic obstructive pulmonary disease and other respiratory disorders.

ABSTRACT OBJECTIVE To evaluate the relationship between ambient air pollutants and chronic obstructive pulmonary disease in relatively low-polluted areas in China. METHODS Atmospheric pollutants levels and meteorological data were obtained from January 2016 to December 2020. The medical database including daily hospital admissions for chronic obstructive pulmonary disease (ICD10: J44) was derived from the First Affiliated Hospital of Gannan Medical University. The generalized additive model was used to analyze the percentage change with 95% confidence interval in daily hospital admissions for chronic obstructive pulmonary disease associated with a 10 µg/m3 increase in atmospheric pollutants levels. RESULTS In total, occurred 4,980 chronic obstructive pulmonary disease hospital admissions (not including emergency department visits) during 2016–2020. The mean concentrations of daily PM2.5, PM10, SO2, NO2, O3, and CO were 37.5 μg/m3, 60.1 μg/m3, 18.7 μg/m3, 23.5 μg/m3, 70.0 μg/m3, and 1.2 mg/m3 in Ganzhou. Each 10 µg/m3 increment of PM2.5, PM10, NO2, and O3 were significantly associated with 2.8% (95%CI: 1.0–4.7), 1.3% (95%CI: 0.3–2.4), 2.8% (95%CI: 0.4–5.4), and 1.5% (95%CI: 0.2–2.7) elevation in daily chronic obstructive pulmonary disease hospital admissions. The estimates of delayed effects of PM2.5, PM10, NO2, and O3 were observed at lag6, lag6, lag8, lag1, respectively. The health effects of particulate pollutants (PM2.5 and PM10) may be independent of other pollutants. The adverse effects of air pollutants were more evident in the warm season (May–Oct) than in the cold season (Nov–Apr). CONCLUSION Our study demonstrated that elevated concentrations of atmospheric pollutant (PM2.5, PM10, NO2, and O3), especially particulate pollutants, can be associated with increased daily count of hospital admissions for chronic obstructive pulmonary disease , which may promote further understanding of the potential hazards of relatively low levels of air pollution on chronic obstructive pulmonary disease and other respiratory disorders.