Air pollution is the entry or inclusion of living things, energy substances, and other components into the air. Moreover, Air pollution is the presence of one or several contaminants in the outside atmospheric air such as dust, foam, gas, fog, smoke or steam in large quantities with various properties and time intervals of the contaminants in the air resulting in disturbances to the lives of humans, plants or animals. One of the parameters measured in determining air quality is PM 2.5. However, PM 2.5 has a higher probability of being able to enter the lower respiratory tract because small particle diameters can potentially pass through the lower respiratory tract. In this paper, we will get two different insight. First, the probability of status change using Markov chain and second, forecasting by using VAR-NN-PSO. More details we classify by three classifications no risk (1-30), medium risk (30-48), and moderate (>49) in Pingtung and Chaozhou. This data is starting from January 2014 to May 2019 and it can be modeled with the Markov chain. At the same time, we perform Hybrid VAR-NN-PSO to forecast PM 2.5 in Pingtung and Chaozhou. In this optimization, the search for best solutions is carried out by a population consisting of several particles. Based on the results of the discussion, opportunities for the transition from monthly status change are obtained continuous stochastic time with a stationary probability distribution. Regarding the VAR-NN-PSO, we obtained the mean absolute percentage error (MAPE) 3.57% for PM 2.5 data in Pingtung and 4.87% for PM 2.5 data in Chaozhou, respectively. This model can be predicted to forecasting 180 days ahead. Besides, the population in PSO has generated randomly with the smallest value and the high value the accuracy.
Background and objectives: The impacts of COVID-19 are like two sides of one coin. During 2020, there were many research papers that proved our environmental and climate conditions were improving due to lockdown or large-scale restriction regulations. In contrast, the economic conditions deteriorated due to disruption in industry business activities and most people stayed at home and worked from home, which probably reduced the noise pollution. Methods: To assess whether there were differences in noise pollution before and during COVID-19. In this paper, we use various statistical methods following odds ratios, Wilcoxon and Fisher’s tests and Bayesian Markov chain Monte Carlo (MCMC) with various comparisons of prior selection. The outcome of interest for a parameter in Bayesian inference is complete posterior distribution. Roughly, the mean of the posterior will be clear with point approximation. That being said, the median is an available choice. Findings: To make the Bayesian MCMC work, we ran the sampling from the conditional posterior distributions. It is straightforward to draw random samples from these distributions if they have regular shapes using MCMC. The case of over-standard noise per time frame, number of noise petition cases, number of industry petition cases, number of motorcycles, number of cars and density of vehicles are significant at α=5%. In line with this, we prove that there were differences of noise pollution before and during COVID-19 in Taiwan. Meanwhile, the decreased noise pollution in Taiwan can improve quality of life.
The exposure rate to air pollution in most urban cities is really a major concern because it results to a life-threatening consequence for human health and wellbeing. Furthermore, the accurate estimation and continuous forecasting of pollution levels is a very complicated task. In this paper, one of the space-temporal models, a vector autoregressive (VAR) with neural network (NN) and genetic algorithm (GA) was proposed and enhanced. The VAR could tackle the issue of multivariate time series, NN for nonlinearity, and GA for parameter estimation determination. Therefore, the model could be used to make predictions, such as the information of series and location data. The applied methods were on the pollution data, including NOX, PM2.5, PM10, and SO2 in Taipei, Hsinchu, Taichung, and Kaohsiung. The metaheuristics genetic algorithm was used to enhance the proposed methods during the experiments. In conclusion, the VAR-NN-GA gives a good accuracy when metric evaluation is used. Furthermore, the methods can be used to determine the phenomena of 10 years air pollution in Taiwan.
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