ABSTRACT
Towards the end of 2019, a novel contagious virus (COVID-19) came out of Wuhan, China and turned into a disastrous pandemic. Many countries were locked down;completely or partially. The ongoing pandemic not only affected our economies and routine life, but also the environment. This study was aimed to compare the air quality of the Indian subcontinent prior to and during the COVID-19 pandemic. In this regard, air quality parameters (ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide, PM2.5 and PM10) and meteorological parameters (wind speed and relative humidity) were analysed. The data was obtained from 229 monitoring stations in India and satellitebased Aerosol Absorption Index (AAI) during the springs of 2019 and 2020. The result indicated a significant decline in the concentration mean, six air pollutants (i.e., PM2.5, PM10, N2, SO2, O3 and CO) decreased by 36.27, 42.96, 44.62, 28.88, 18.35 and 20.51 %, respectively during April 2020 due to less to no industrial activities and vehicular emissions. The spatial variation of each parameter was simulated using the Inverse Distance Weighted (IDW) interpolation method. An Analytical Hierarchy Process (AHP) model was applied to generate the overall air quality severity zonation map of the country. The zonation map indicated that by adopting cleaner fuel and restriction on biomass burning in the rural and urban sectors can improve the ambient air quality © 2023, Suranaree Journal of Science and Technology.All Rights Reserved.
ABSTRACT
To address challenges associated with climate change, population growth and decline in international trade linked to the COVID-19 pandemic, determining whether national crop production can meet populations' requirements and contribute to socio-economic resilience is crucial. Three crop models and three global climate models were used in conjunction with predicted population changes. Compared with wheat production in 2000-2010, total production and per capita wheat production were significantly (P < 0.05) increase in 2020-2030, 2030-2040 and 2040-2050, respectively, under RCP4.5 and RCP8.5 due to climate change in China. However, when considering population and climate changes, the predicted per capita production values were 125.3 ± 0.3, 127.1 ± 2.3 and 128.8 ± 2.7 kg during the 2020-2030, 2030-2040, 2040-2050 periods under RCP4.5, or 126.2 ± 0.7, 128.7 ± 2.5, and 131.0 ± 4.1 kg, respectively, under RCP8.5. These values do not significantly differ (P > 0.05) from the baseline level (127.9 ± 1.3 kg). The average per capita production in Loess Plateau and Gansu-Xinjiang subregions declined. In contrast, per capita production in the Huanghuai, Southwestern China, and Middle-Lower Yangtze Valleys subregions increased. The results suggest that climate change will increase total wheat production in China, but population change will partly offset the benefits to the grain market. In addition, domestic grain trade will be influenced by both climate and population changes. Wheat supply capacity will decline in the main supply areas. Further research is required to address effects of the changes on more crops and in more countries to obtain deeper understanding of the implications of climate change and population growth for global food production and assist formulation of robust policies to enhance food security. Supplementary Information: The online version contains supplementary material available at 10.1007/s12571-023-01351-x.
ABSTRACT
BACKGROUND: The novel coronavirus disease 2019 (COVID-19) has spread rapidly around the world since December 8, 2019. However, the key factors affecting the duration of recovery from COVID-19 remain unclear. OBJECTIVE: To investigate the associations of long recovery duration of COVID-19 patients with ambient air pollution, temperature, and diurnal temperature range (DTR) exposure. METHODS: A total of 427 confirmed cases in Changsha during the first wave of the epidemic in January 2020 were selected. We used inverse distance weighting (IDW) method to estimate personal exposure to seven ambient air pollutants (PM2.5, PM2.5-10, PM10, SO2, NO2, CO, and O3) at each subject's home address. Meteorological conditions included temperature and DTR. Multiple logistic regression model was used to investigate the relationship of air pollution exposure during short-term (past week and past month) and long-term (past three months) with recovery duration among COVID-19 patients. RESULTS: We found that long recovery duration among COVID-19 patients was positively associated with short-term exposure to CO during past week with OR (95% CI) = 1.42 (1.01-2.00) and PM2.5, NO2, and CO during past month with ORs (95% CI) = 2.00 (1.30-3.07) and 1.95 (1.30-2.93), and was negatively related with short-term exposure to O3 during past week and past month with ORs (95% CI) = 0.68 (0.46-0.99) and 0.41 (0.27-0.62), respectively. No association was observed for long-term exposure to air pollution during past three months. Furthermore, increased temperature during past three months elevated risk of long recovery duration in VOCID-19 patients, while DTR exposure during past week and past month decreased the risk. Male and younger patients were more susceptible to the effect of air pollution on long recovery duration, while female and older patients were more affected by exposure to temperature and DTR. CONCLUSION: Our findings suggest that both TRAP exposure and temperature indicators play important roles in prolonged recovery among COVID-19 patients, especially for the sensitive populations, which provide potential strategies for effective reduction and early prevention of long recovery duration of COVID-19.