A Novel Framework for Forecasting, Evaluation and Early-Warning for the Influence of PM10 on Public Health

Abstract

PM2.5 has attracted widespread attention since the public has become aware of it, while attention to PM10 has started to wane. Considering the significance of PM10, this study takes PM10 as the research object and raises a significant question: when will the influence of PM10 on public health end? To answer the abovementioned question, two promising research areas, i.e., air pollution forecasting and health effects analysis, are employed, and a novel hybrid framework is developed in this study, which consists of one effective model and one evaluation model. More specifically, this study first introduces one advanced optimization algorithm and cycle prediction theory into the grey forecasting model to develop an effective model for multistep forecasting of PM10, which can achieve reasonable forecasting of PM10. Then, an evaluation model is designed to evaluate the health effects and economic losses caused by PM10. Considering the significance of providing the future impact of PM10 on public health, we extend our forecasting results to evaluate future changes in health effects and economic losses based on our proposed health economic losses evaluation model. Accordingly, policymakers can adjust current air pollution prevention plans and formulate new plans according to the results of forecasting, evaluation and early-warning. Empirical research shows that the developed framework is applicable in China and may become a promising technique to enrich the current research and meet the requirements of air quality management and haze governance.

Research agenda for using artificial intelligence in health governance: interpretive scoping review and framework

“…Stability and harmony of society, economy, and environment are followed by AI towards achieving sustainable development goals (SDGs), [ 30 , 32 , 56 ], as well as the international and national priorities [ 57 ], e.g., analyzing safety, quality of agricultural products, [ 30 ] pollution and their impacts on health and economic losses from different aspects such as losses due to lost work, and medical expenses due to illness [ 30 , 58 ]. A study proved geospatial system as a viable tool for assisting governmental agencies to devise appropriate plans toward SDGs [ 36 ].…”

“…AI allows economic loss estimation based on exposure to pollution, and its impacts on health status. Different health-related outcomes including mortality, individuals' daily functions, emergency cases, children with lower respiratory tract infection/asthma, outpatient rate of respiratory disease, asthma cases, and chronic bronchitis can be considered [ 58 ]. In another study, researchers explored the influencing factors of air pollution on government health expenditure and spatial governance [ 75 ].…”

Feature Selection in MicroArray Datasets using Manta Ray Foraging Optimization Technique

“…The improved MRFO was proposed by Elaziz et al [8] based on differential evolution as a method for selecting features to identify COVID-19 patients examining pictures of their chests. To predict and assess the impact of PM10 on public health, Yang et al [27] introduced an MRFO based hybrid framework using grey prediction theory. The question is whether we need yet another novel optimization strategy because there are so many optimization algorithms in the literature that work fairly effectively.…”

The Application of Artificial Intelligence in Health Policy: A Scoping Review

“…In one study, researchers present a model to evaluate its future effects on health and economic losses. The possibility of formulating new strategies, policies, and plans according to the results of forecasting, evaluation, and early warning [74] or predicting and informing public health interventions [75] will change policymaking. Several studies have examined Media coverage to support policies [76].…”