Explore a Multivariate Bayesian Uncertainty Processor driven by artificial neural networks for probabilistic PM2.5 forecasting

Evolution of neural network to deep learning in prediction of air, water pollution and its Indian context

The scenario of developed and developing countries nowadays is disturbed due to modern living style which affects environment, wildlife and natural habitat. Environmental quality has become or is a subject of major concern as it is responsible for health hazard of mankind and animals. Measurements and prediction of hazardous parameters in different fields of environment is a recent research topic for safety and betterment of people as well as nature. Pollution in nature is an after-effect of civilization. To combat the damage already happened, some processes should be evolved for measurement and prediction of pollution in various fields. Researchers of all over the world are active to find out ways of predicting such hazard. In this paper, application of neural network and deep learning algorithms is chosen for air pollution and water pollution cases. The purpose of this review is to reveal how family of neural network algorithms has applied on these two pollution parameters. In this paper, importance is given on algorithm, and datasets used for air and water pollution as well as the predicted parameters have also been noted for ease of future development. One major concern of this paper is Indian context of air and water pollution research, and the research potential presents in this area using Indian dataset. Another aspect for including both air and water pollutions in one review paper is to generate an idea of artificial neural network and deep learning techniques which can be cross applicable for future purpose.

Develop a hybrid machine learning model for promoting microbe biomass production

Since the cultivation condition of microbe biomass production (mycelia yield) involves a variety of factors, it's a laborious process to obtain the optimal cultivation condition of Antrodia cinnamomea (A. cinnamomea). This study proposed a hybrid machine learning approach (i.e., ANFIS-NM) to identify the potent factors and optimize the cultivation conditions of A. cinnamomea based on a 32 fractional factorial design with seven factors. The results indicate that the ANFIS-NM approach successfully identified three key factors (i.e., glucose, potato dextrose broth, and agar) and significantly boosted mycelia yield. The interpretability of ANFIS rules made the cultivation conditions visually interpretable. Subsequently, a three-factor five-level central composite design was used to probe the optimal yield. This study demonstrates the proposed hybrid machine learning approach could significantly reduce the time consumption in laboratory cultivation and increase mycelia yield that meets SDGs 7 and 12, hitting a new milestone for biomass production.

Modeling air pollution by integrating ANFIS and metaheuristic algorithms

Air pollution is increasing for many reasons, such as the crowding of cities, the failure of planning to consider the benefit of society and nature, and the non-implementation of environmental legislation. In the recent era, the impacts of air pollution on human health and the ecosystem have become a primary global concern. Thus, the prediction of air pollution is a crucial issue. ANFIS is an artificial intelligence technique consisting of artificial neural networks and fuzzy inference systems, and it is widely used in estimating studies. To obtain effective results with ANFIS, the training process, which includes optimizing its premise and consequent parameters, is very important. In this study, ANFIS training has been performed using three popular metaheuristic methods: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) for modeling air pollution. Various air pollution parameters which are particular matters: PM2.5 and PM10, sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), and several meteorological parameters such as wind speed, wind gust, temperature, pressure, and humidity were utilized. Daily air pollution predictions in Istanbul were obtained using these particular matters and parameters via trained ANFIS approaches with metaheuristics. The prediction results from GA, PSO, and DE-trained ANFIS were compared with classical ANFIS results. In conclusion, it can be said that the trained ANFIS approaches are more successful than classical ANFIS for modeling and predicting air pollution.

Real-time image-based air quality estimation by deep learning neural networks

Air quality profoundly impacts public health and environmental equity. Efficient and inexpensive air quality monitoring instruments could be greatly beneficial for human health and air pollution control. This study proposes an image-based deep learning model (CNN-RC) that integrates a convolutional neural network (CNN) and a regression classifier (RC) to estimate air quality at areas of interest through feature extraction from photos and feature classification into air quality levels. The models were trained and tested on datasets with different combinations of the current image, the baseline image, and HSV (hue, saturation, value) statistics for increasing model reliability and estimation accuracy. A total of 3549 hourly air quality datasets (including photos, PM2.5, PM10, and the air quality index (AQI)) collected at the Linyuan air quality monitoring station of Kaohsiung City in Taiwan constituted the case study. The main breakthrough of this study is to timely produce an accurate image-based estimation of several pollutants simultaneously by using only one single deep learning model. The test results show that estimation accuracy in terms of R² for PM_2.5, PM_10, and AQI based on daytime (nighttime) images reaches 76% (83%), 84% (84%), and 76% (74%), respectively, which demonstrates the great capability of our method. The proposed model offers a promising solution for rapid and reliable multi-pollutant estimation and classification based solely on captured images. This readily scalable measurement approach could address major gaps between air quality data acquired from expensive instruments worldwide.

Explore Regional PM2.5 Features and Compositions Causing Health Effects in Taiwan

Chemical compositions of atmospheric fine particles like PM2.5 prove harmful to human health, particularly to cardiopulmonary functions. Multifaceted health effects of PM2.5 have raised broader, stronger concerns in recent years, calling for comprehensive environmental health-risk assessments to offer new insights into air-pollution control. However, there have been few studies adopting local air-quality-monitoring datasets or local coefficients related to PM2.5 health-risk assessment. This study aims to assess health effects caused by PM2.5 concentrations and metal toxicity using epidemiological and toxicological methods based on long-term (2007-2017) hourly monitoring datasets of PM2.5 concentrations in four cities of Taiwan. The results indicated that (1) PM2.5 concentrations and hazardous substances varied substantially from region to region, (2) PM2.5 concentrations significantly decreased after 2013, which benefited mainly from two actions against air pollution, i.e., implementing air-pollution-control strategies and raising air-quality standards for certain emission sources, and (3) under the condition of low PM2.5 concentrations, high health risks occurred in eastern Taiwan on account of toxic substances adsorbed on PM2.5 surface. It appears that under the condition of low PM2.5 concentrations, the results of epidemiological and toxicological health-risk assessments may not agree with each other. This raises a warning that air-pollution control needs to consider toxic substances adsorbed in PM2.5 and region-oriented control strategies are desirable. We hope that our findings and the proposed transferable methodology can call on domestic and foreign authorities to review current air-pollution-control policies with an outlook on the toxicity of PM2.5.

Coupling mobile phone data with machine learning: How misclassification errors in ambient PM2.5 exposure estimates are produced?

BACKGROUND

Most studies relying on time-activity diary or traditional air pollution modelling approach are insufficient to suggest the impacts of ignoring individual mobility and air pollution variations on misclassification errors in exposure estimates. Moreover, very few studies have examined whether such impacts differ across socioeconomic groups.

OBJECTIVES

We aim to examine how ignoring individual mobility and PM2.5 variations produces misclassification errors in ambient PM2.5 exposure estimates.

METHODS

We developed a geo-informed backward propagation neural network model to estimate hourly PM2.5 concentrations in terms of remote sensing and geospatial big data. Combining the estimated PM2.5 concentrations and individual trajectories derived from 755,468 mobile phone users on a weekday in Shenzhen, China, we estimated four types of individual total PM2.5 exposures during weekdays at multi-temporal scales. The estimate ignoring individual mobility, PM2.5 variations or both was compared with the hypothetical error-free estimate using paired sample t-test. We then quantified the exposure misclassification error using Pearson correlation analysis. Moreover, we examined whether the misclassification error differs across different socioeconomic groups. Taking findings of ignoring individual mobility as an example, we further investigated whether such findings are robust to the different selections of time.

RESULTS

We found that the estimate ignoring PM2.5 variations, individual mobility or both was statistically different from the hypothetical error-free estimate. Ignoring both factors produced the largest exposure misclassification error. The misclassification error was larger in the estimate ignoring PM2.5 variations than that ignoring individual mobility. People with high economic status suffered from a larger exposure misclassification error. The findings were robust to the different selections of time.

CONCLUSIONS

Ignoring individual mobility, PM2.5 variations or both leads to misclassification errors in ambient PM2.5 exposure estimates. A larger misclassification error occurs in the estimate neglecting PM2.5 variations than that ignoring individual mobility, which is seldom reported before.

Human activities and the natural environment have induced changes in the PM2.5 concentrations in Yunnan Province, China, over the past 19 years

Fine particulate matter (PM_2.5) concentrations exhibit distinct spatiotemporal heterogeneity, mainly due to the natural environment and human activities. Yunnan Province of China was selected as the research area, and a real-time measured PM_2.5 concentration dataset was acquired from 41 monitoring stations in 16 major cities from February 2013 to December 2018. Aerosol optical depth (AOD) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and data on four meteorological variables from 2000 to 2018 were employed. A novel hybrid model was constructed to estimate the historical missing PM_2.5 values from 2000 to 2012, calculate the missing PM_2.5 concentrations from 2012 to 2014 in some major cities, and analyze the driving factors of the PM_2.5 concentration changes and causes of key pollution events in Yunnan Province over the past 19 years. The temporal analysis results indicate that the annual mean PM_2.5 concentration in Yunnan Province exhibited three stages: continuous stability, a rapid increase and a rapid decrease. The year 2013 was an important breakpoint in the trend of the concentration change. The spatial analysis results reveal that the annual mean PM_2.5 concentration in the north was lower than that in the south, and there was a significant difference between the east and the west. In addition, springtime biomass burning in Southeast Asia was found to be the main cause of PM_2.5 pollution in Yunnan Province in spring.