ABSTRACT
This paper investigated the influence and interactions of air pollution concentrations by using the stochastic boosted regression trees between variables for each station and the impact of the COVID-19 Movement Control Order at Ipoh City air quality station. The one-hour data were gathered from the Department of Environment from January until June 2019 and 2020. Two thousand two hundred thirty-one data of particles, gases (Nitrogen oxides, Sulphur Dioxide, Ozone, Carbon Monoxide) concentrations and meteorological data (wind speed, wind directions, temperature, and relative humidity) were captured. The BRT model development process with an algorithm using a comprehensive package, R Software and its packages to understand the variability and trends. It was found that the relationship between the number of samples and number of trees (nt) of 4372 for oob were found the best iterations obtained. The performance of the boosting model was assessed and found that the FAC2 was 0.91, the R2 values were above 0.56 (R = 0.74), and the Index of Agreements (IOA) was 0.67, which fall ranges are within an acceptable for model performance. The Relative Variable Importance (RVI) that influenced PM2.5 for non-MCO data was CO (18.9% ), SO2 (14.6 %), O3 (12.9 %), and wd (10.66 %) while CO (22.6%), RH (13.4%), 14.7% and O3 (12.1%) were RVI factors influenced to PM2.5 concentrations during MCO periods. Estimating the strength of interaction effects (SIE) between variables was 0.24 for CO-wind directions, followed by 0.19 for ozone-wind speeds and 0.15 for NO2-CO. Results showed that the model developed was within the acceptable range and could be used to understand particles and identify important parameters that influence particle concentrations.
ABSTRACT
High developed industry regions, provincial centers with a heavy traffic and dense populations with cold winters, using low-quality fossil fuel consumption have an effect on quality of life especially for people with respiratory diseases. The air quality data gathered from air monitoring stations for the City Center of Konya, Turkey were analysed statistically during the period when curfew due to the Covid-19 pandemic in 2020. The restrictions that cause the reduction of vehicle exhaust emissions, which are important factors in the formation of some air pollutants, are thought to be effective in improving the air quality as well as meteorological conditions are effective on the days when the air quality is analysed. It is thought that the reduction in HC, NOx, CO and PM emissions, which can be evaluated as exhaust emissions, will be an effective factor due to the restrictions of Covid-19 pandemic. Comparing between the 2019 and 2020 MCO data, we identified that most of the gases decreased with NO2 (-24 µg/m3), SO2 (-24 µg/m3), CO (-37 µg/m3) and an increment of O3 of +50 µg/m3 which indicates that the MCO and restricted of movement were give an impact to air quality levels in Konya City. The increase in O3 values were found by the existing of the sun rays in the atmosphere with the formation of O3 during the clean air period.
ABSTRACT
Objective: Examination of the diagnostic covid-19 gold standard with real time reverse polymerase chain reaction (RT-PCR) is still limited, so that the initial hematological examination (leukocytes, lymphocytes, neutrophils, platelets, hemoglobin, and neutrophil lymphocyte ratio (NLR) plays an important role in monitoring the course of covid-19 disease Macrophages release nitric oxide (NO) to kill parasites NO inhibits migration and adhesion of leucocytes to the endothelium. Metode: The research design used pre-experimental with one group pre-post test, the total sample was 80 health workers who were exposed to Covid-19, the independent variable was the provision of NO and the dependent variable was the result of a hematological examination (leukocytes, lymposites, platelets, neutrophils, hemoglobin and NLR). The intervention was given NO 500 mg mixed with warm water 250 cc, given 3 times a day for 5 days, data analysis used the T-test with a significance value of a<0.05. Results: There is a difference in the results of the pre and post-test hematology giving NO to the results of the examination of leucocytes a=0.001, lymphocytes a=0.000, platelets a=0.000, neutrophils a=0.000, hemoglobin a=0.031, and NLR a=0.000. Conclusion: Giving NO to health workers exposed to Covid-19 can improve the hematology and immune systems to fight the corona virus.