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Air pollution in China is becoming more serious especially for the particular matter (PM) because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China) are used to present a new hybrid model CI-FPA-SVM to forecast air PM2.5 and PM10 concentration in this paper. The proposed model involves two parts. Firstly, due to its deficiency to assess the possible correlation between different variables, the cointegration theory is introduced to get the input-output relationship and then obtain the nonlinear dynamical system with support vector machine (SVM), in which the parameters c and g are optimized by flower pollination algorithm (FPA). Six benchmark models, including FPA-SVM, CI-SVM, CI-GA-SVM, CI-PSO-SVM, CI-FPA-NN, and multiple linear regression model, are considered to verify the superiority of the proposed hybrid model. The empirical study results demonstrate that the proposed model CI-FPA-SVM is remarkably superior to all considered benchmark models for its high prediction accuracy, and the application of the model for forecasting can give effective monitoring and management of further air quality.

Few studies evaluate the amount of particulate matter less than 2.5 mm in diameter (PM₂.₅) in relation to a change in lung function among adults in a population. The aim of this study was to assess the association of coal as a domestic energy source to pulmonary function in an adult population in inner-city areas of Zunyi city in China where coal use is common. In a cross-sectional study of 104 households, pulmonary function measurements were assessed and compared in 110 coal users and 121 non-coal users (≥18 years old) who were all nonsmokers. Several sociodemographic factors were assessed by questionnaire, and ventilatory function measurements including forced vital capacity (FVC), forced expiratory volume in 1 s (FEV₁), the FEV₁/FVC ratio, and peak expiratory flow rate (PEFR) were compared between the 2 groups. The amount of PM₂.₅ was also measured in all residences. There was a significant increase in the relative concentration of PM₂.₅ in the indoor kitchens and living rooms of the coal-exposed group compared to the non-coal-exposed group. In multivariate analysis, current exposure to coal smoke was associated with a 31.7% decrease in FVC, a 42.0% decrease in FEV₁, a 7.46% decrease in the FEV₁/FVC ratio, and a 23.1% decrease in PEFR in adult residents. The slope of lung function decrease for Chinese adults is approximately a 2-L decrease in FVC, a 3-L decrease in FEV₁, and an 8 L/s decrease in PEFR per count per minute of PM₂.₅ exposure. These results demonstrate the harmful effects of indoor air pollution from coal smoke on the lung function of adult residents and emphasize the need for public health efforts to decrease exposure to coal smoke.

A parameter identification problem for a hybrid model is presented. The latter describes the operation of an activated sludge process used for waste water treatment. Parameter identification problem can be considered as an optimization one by minimizing the error between simulation and experimental data. One of the new and promising metaheuristic methods for solving similar mathematical problem is Cuckoo Search Algorithm. It is inspired by the parasitic brood behavior of cuckoo species. To confirm the effectiveness and the efficiency of the proposed algorithm, simulation results will be compared with other algorithms, firstly, with a classical method which is the Nelder-Mead algorithm and, secondly, with intelligent methods such as Genetic Algorithm and Particle Swarm Optimization approaches.

A rapid rise in inhabitants across the globe has led to the inadmissible management of waste in various countries, giving rise to various health issues and environmental pollution. The waste‐collecting trucks collect waste just once or twice in seven days. Due to improper waste collection practices, the waste in the dustbin is spread on the streets. Thus, to defeat this situation, an efficient solution for smart and effective waste management using machine learning (ML) and the Internet of Things (IoT) is proposed in this paper. In the proposed solution, the authors have used an Arduino UNO microcontroller, ultrasonic sensor, and moisture sensor. Using image processing, one can measure the waste index of a particular dumping ground. A hardware prototype is also developed for the proposed framework. Thus, the presented solution for the efficient management of waste accomplishes the aim of establishing clean and pollution‐free cities.

Background Epidemiological studies suggest that road traffic noise increases the risk of stroke. Similar effects may be expected from wind turbine noise ( WTN ) exposure, but epidemiological evidence is lacking. The present study investigated the association between long‐term exposure to WTN and the risk for stroke. Methods and Results First‐ever stroke in 28 731 female nurses in the Danish Nurse Cohort was identified in the Danish National Patient register until the end of 2013. WTN , traffic noise, and air pollution exposures were estimated for all historic and present residential addresses between 1982 and 2013. Time‐varying Cox proportional hazard regression was used to examine the associations between the 11‐, 5‐, and 1‐year rolling means of WTN levels and stroke incidence. Of 23 912 nurses free of stroke at the cohort baseline, 1097 nurses developed stroke by the end of follow‐up. At the cohort baseline, 10.3% of nurses were exposed to WTN (≥1 turbine within a 6000‐meter radius of the residence) and 13.3% in 2013. Mean baseline residential noise levels among exposed nurses were 26.3 dB (A). No association between long‐term WTN exposure and stroke incidence was found. The adjusted hazard ratios and 95% CIs for the 11‐, 5‐, and 1‐year running mean residential WTN exposures preceding stroke diagnosis, comparing nurses with residential WTN levels above and below 20 dB (A) were 1.09 (0.90–1.31), 1.08 (0.89–1.31) and 1.08 (0.89–1.32), respectively. Conclusions This comprehensive cohort study lends no support to an association between long‐term WTN exposure and stroke risk.

My country’s current research on the influencing factors of total factor productivity has problems such as single evaluation method, low efficiency, and poor overall level in terms of evaluation methods and evaluation efficiency. Based on this, this study divides the financial structure into three traditional sections, banking, securities, and insurance, and uses the DEA model to study the temporal and spatial differences of the financial structure’s influence on the total factor productivity of the four major political and economic regions of China’s eastern, western, central, and northeastern China. First, establish a DEA model based on data mining algorithms, combine financial data comparisons over the years, to achieve a quantitative analysis of the financial structure’s impact on China’s total factor productivity, calculate financial efficiency, and then combine the DEA analysis data model with the grey correlation method. Analyze its internal influence rules, and design experiments for model verification analysis. The results show that the DEA analysis model can realize 8 iterations of data on the impact of financial structure on China’s total factor productivity, and its evaluation accuracy can reach more than 96.2%.

Green infrastructure is the application of nature-based solutions like bioswales, rain gardens, and permeable pavements to reduce flooding in urban areas. These systems are underutilized in the design of the built environment. A barrier to their implementation is that design engineers tend to discount the tangential benefits of these greener systems and overweigh the associated risks. This study tested whether priming engineers to think about the environmental and social sustainability benefits of green infrastructure can influence what attributes engineers consider and how they weigh these attributes during the design decision-making process. Forty engineering students trained in stormwater design were asked to evaluate the implementation of a conventional stormwater design option and a green stormwater design option. Their preferred design option was recorded and the changes in their neuro-cognition were measured using functional near infrared-spectroscopy. Half of the engineers were asked to first consider the potential outcomes of these options on the environment and the surrounding community. Priming engineers to first consider environmental and social sustainability before considering the cost and risk of each option, significantly increased the perceived benefits the engineers believed green infrastructure could provide. The priming intervention also increased the likelihood that engineers would recommend the green infrastructure option. The engineers primed to think about environmental and social sustainability exhibited significantly lower oxy-hemoglobin in their ventrolateral, dorsolateral, and medial prefrontal cortex through multiple phases of the judgment and decision-making process. The intervention appears to increase cognitive representativeness or salience of the benefits for green infrastructure when engineers evaluate design alternatives. This relatively low-cost intervention, asking engineers to consider environmental and social sustainability for each design alternative, can shift engineering decision-making and change neuro-cognition.