Assessing the Spatial Equality of COVID Testing Sites Maintaining Zero COVID Policy

Rapid and timely testing is essential to minimize the COVID-19 spread. Decision makers and policy planners need to determine the equal distribution and accessibility of testing sites. This study mainly examines the spatial equality of COVID-19 testing sites that maintain a zero COVID policy in Guangzhou City. The study has identified the spatial disparities of COVID testing sites, characteristics of testing locations, and accessibility. The study has obtained information on COVID testing sites in Guangzhou City and population data. Point pattern analyses, Euclidian distance and allocation, and network analyses are the main methods used to achieve the research objectives, and 1183 total COVID testing sites can be recognized in Guangzhou City. Results revealed that spatial disparities could be noticed over the study area. Testing locations of Guangzhou City are highly clustered. The most significant testing sites are located in Haizhu District, which has the third largest population. The highest population density can be identified in Yuexiu District. However, only 94 testing sites are located there. According to all the results, higher disparities can be identified, and a lack of testing sites is located in the north part of the study area. Some people in the northern part have to travel more than 10 km to reach a testing site. Finally, this paper suggests increasing the number of testing sites in the north and south parts of the study area and keeping the same distribution, considering the area, total population, and population density. This kind of research will be helpful to decision-makers in making proper decisions to maintain a zero COVID policy.

Exploring the Relationship between Spatiotemporal Variations in Air Quality and Meteorological Parameters before and during the COVID-19 Pandemic in Xi’an

The COVID-19 pandemic has significantly changed the air pollution of the world. The present study investigated the temporal and spatial variability in air quality in Xi’an, China, and its relationship with meteorological parameters during and before the COVID-19 pandemic. The outcomes of this study indicated that air pollutants, PM2.5, NO2, PM10, CO, and SO2 are likely to decrease during winter (25%, 50%, 30%, 40%, and 35%) to spring (30%, 55%, 38%, 50%, and 40%) and summer (40%, 58%, 60%, 55%, and 47%), respectively. However, the concentration of O3-8h increased by 40%, 55%, and 65% during winter, spring, and summer, respectively. The values of the air quality index decreased during the COVID-19 period. Furthermore, significant positive trends were reported in PM2.5, NO2, PM10, O3, and SO2, and no notable trends in CO during the COVID-19 pandemic. Both during and before the COVID-19 period, PM10, NO2, PM2.5, CO, and SO2 showed a negative correlation with the temperature and a moderately positive significant correlation between O3-8h and temperature. The findings of this study would help understand the air pollution circumstances in Xi’an before and during the COVID-19 period and offer helpful information regarding the implications of different air pollution control strategies.

The Prediction of Carbon Emissions Demands in India under the Balance Economic Growth Path

Facing the challenge of climate change, forecasts of energy demand and carbon emissions demand are a key requirement for India to ensure energy security and the balance economic growth. The authors calculate the optimal economic growth under the balance economic growth path from 2009 to 2050 in India based on the economy-carbon dynamic model. Combination of Intergovernmental Panel on Climate Change (IPCC) 2006 edition of the formula of carbon emissions, energy intensity model, and population model, it gets the carbon emissions demand caused by energy consumption for time span 1980-2008. Then, it estimates the energy consumption demand and carbon emissions demand under the balance economic growth path from 2009 to 2050. The results show that the cumulative amount of energy demand and carbon emissions demand in India for the time span 2009 to 2050, are 44.65 Gtoe and 36.16 Gt C, separately. The annual demand of energy consumption and carbon emissions for India show an inverted U curve from 2009 to 2050. The demand of energy consumption and carbon emissions will peak in 2045, and the peak values are 1290.74 Mtoe and 1045.98 Mt C. Furthermore, India’s per capita energy consumption demand and carbon emissions demand also appear maximum values, which are separately 0.81 toe and 0.65 t C.