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With the technical advancements in Deep Learning (DL), it is probable to construct the predictor model for monitoring and controlling pollution from real-time data. Here, IoT techniques are used for sensing the emission rate from various factors and the predictor model is constructed using the available data, for instance, carbon monoxide prediction. Modern sensors are embedded to evaluate the level of pollutants and using these modern techniques, the source of emission rate is identified and notified to the specific environment. Deep learning concepts are used for predicting the pollution level based on the current and previous data attained from the sensors. Here, we have implemented a learning solution to predict carbon monoxide concentration hourly using the novel Dense Residual Convolutional Network Model with Bi-LSTM (Bidirection-Long Short Term Memory) with the spatial and temporal features by integrating the features of the present and previous pollutant data. The side output from the residual network model is used to evaluate prediction quality. The performance is compared with existing approaches like standard LSTM, CNN, pre-trained network model, etc. The experimentation is done in a Python environment, and the proposed model facilitates more prediction accuracy for the pollutants CO,SO_2,O_3 and NO_2 than other conventional network models and establishes a better trade-off.

The present study aimed to compare the concentration of 14 metals in the air of 4 cities of Khuzestan province (Ahvaz, Dezful, Mahshahr, and Abadan) in dusty and non-dustynon-dustyy conditions, and determine risk and non-carcinogenic risk in a six-month period from 2017 to 2018 (autumn, winter, and spring). A total of 72 samples were collected in dusty and 72 samples in non-dustydusty conditions at different locations and times using a high volume sampling pump with 110 lit/s flow rate and a fiberglass filter with the pure size of 1μm for 6 hours. Additionally, the concentration of metals was measured using the Spectro Acros Model (Germany) device. Risk assessment and probable ways of entering the body of these metals were determined using USEPA indicators. The average concentration of metals (except for Ni, Mg, and Mn) was higher in dusty conditions non-dustythan in non-dustyy conditions (p < 0.05).The concentration order of heavy metals in dusty and non-dustyy condictions was Zn>Mn>Pb>Ni>Cr>Fe>Cu, and Mg >Mn>Pb>Ni>Cr>Fe, respectively, showing the origin of Zn in the polluted air caused by dust and pollution transfer from outside into cities by this phenomenon. In children and adults, every four cities of Ding (inhalation of dust particles through direct dust consumption) were the main way to attract more metals. Chromium and nickel, chromium and magnesium, and nickel and magnesium were observed among the children of Abadan, Ahvaz, and Dezful and Mahshahr, respectively. The maximum HI indicator was observed for magnesium in Abadan, copper, and magnesium in Ahvaz, chromium, zinc, nickel, and ferrous in Mahshahr in dusty and non-dusty conditions. Moreover, lead had the maximum HI indicator in Abadan and Ahvaz in the non-dustydusty condition. Based on the HI<1 standard, in dusty and clean air conditions, all of the four cities had no concerning issues of non-carcinogenic effects for children and adults.

In order to study the effective methods of green and low-carbon transformation in Chinese cities, this paper takes Rizhao City, Shandong Province, China, as the object of study. Based on the analysis of the research background and the literature review, and taking into account the characteristics of Rizhao City, the following five categories with a total of 25 indicators were selected to construct the assessment indicator system: the environmental quality of green and low-carbon transformation, the quality of the planning and construction of green and low-carbon transformation, the quality of the life and work in the green and low-carbon transformation, the quality of the process of the green transformation empowering the development of high-quality, and the quality of the results of the green transformation empowering high-quality development. Based on this, the two-level fuzzy comprehensive assessment model is reconstructed, and the relevant statistical data provided by the government is used to comprehensively assess the high-quality development performance of the green transformation and empowerment cities in Rizhao City from 2012 to 2022. It is found that the performance of high-quality development empowered by green transformation in Rizhao City, China, has shown a continuous upward trend, having risen from Level Ⅳ in 2012, to Level II by 2022, and remained at Level II during 2018-2022, with its assessment results and showing an upward trend. Finally, based on the specific research results, the policy suggestions to improve the high-quality development performance of cities empowered by the green and low-carbon transformation of Rizhao City, China are discussed.