The Influence of Air Pollution Concentrations on Solar Irradiance Forecasting Using CNN-LSTM-mRMR Feature Extraction

Maintaining a steady power supply requires accurate forecasting of solar irradiance,since clean energy resources do not provide steady power.The existing forecasting studies have examined the limited effects of weather conditions on solar radiation such as temperature and precipitation utilizing convolutional neural network(CNN),but no comprehensive study has been conducted on concentrations of air pollutants along with weather conditions.This paper proposes a hybrid approach based on deep learning,expanding the feature set by adding new air pollution concentrations,and ranking these features to select and reduce their size to improve efficiency.In order to improve the accuracy of feature selection,a maximum-dependency and minimum-redundancy(mRMR)criterion is applied to the constructed feature space to identify and rank the features.The combination of air pollution data with weather conditions data has enabled the prediction of solar irradiance with a higher accuracy.An evaluation of the proposed approach is conducted in Istanbul over 12 months for 43791 discrete times,with the main purpose of analyzing air data,including particular matter(PM10 and PM25),carbon monoxide(CO),nitric oxide(NOX),nitrogen dioxide(NO_(2)),ozone(O₃),sulfur dioxide(SO_(2))using a CNN,a long short-term memory network(LSTM),and MRMR feature extraction.Compared with the benchmark models with root mean square error(RMSE)results of 76.2,60.3,41.3,32.4,there is a significant improvement with the RMSE result of 5.536.This hybrid model presented here offers high prediction accuracy,a wider feature set,and a novel approach based on air concentrations combined with weather conditions for solar irradiance prediction.

Analysis of Characteristics and Meteorological Influencing Factors of Air Pollution in Luojiang District,Deyang City

Based on the monitoring data of ambient air quality and meteorological observation data,the characteristics and meteorological influencing factors of air pollution in Luojiang District of Deyang City from 2018 to 2022 were analyzed.The results show that from 2018 to 2022,the main air pollutants affecting the air quality of Luojiang District of Deyang City were PM_(2.5) and PM_(10),and the primary pollutant on heavy pollution days was basically PM_(2.5).PM_(2.5) and PM_(10) pollution showed obvious seasonal differences,and PM_(2.5) concentration exceeded the limit mainly in spring and winter,among which it was the most serious in early spring,especially in January and February,followed by December.PM_(10) exceeding the standard had a high seasonal correlation with PM_(2.5) exceeding the standard,mainly in spring and winter,among which it was the most serious in winter,especially in December,followed by January.PM_(2.5) and PM_(10) pollution showed an overall weakening trend.PM_(2.5) and PM_(10) concentration were closely related to meteorological factors such as temperature,relative humidity,wind speed,precipitation and air pressure,and were mainly affected by rainfall.

Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems.Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions.Relating to air pollution occurs a main environmental problem in smart city environments.The effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution forecast.Therefore,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart Cities.The projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city environment.To achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input data.For air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL models.The simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.

Analysis of Meteorological Causes of Serious Air Pollution in Xingtai City Based on Inversion Layer

Based on meteorological sounding data in 2011 -2014 and air pollution data in 2013 and 2014 from an environmental monitoring station in Xingtai City, the characteristics of inversion layer and air pollution and their correlation in Xingtai City were discussed. The results show that term perature inversion was very serious at 07：00 in Xingtai City, and days with temperature inversion accounted for above 90%. There were obvious seasonal variations in air pollution in Xingtai City, and air pollution was the most serious in winter but the slightest in summer. The primary air pollu- tants in Xingtai City are PM10 and PM2.5, and the primary air pollutants and AQI had close correlation with parameters of inversion layer. Stable ground inversion layer could hinder air convection and air capacity, so that air pollution become more serious with the discharge of pollutants. Inversion layer nearly appearing all the year around is an important meteorological reason for serious air pollution in Xingtai City.

Real-time air pollution monitoring with sensors on city bus

This paper presents an experimental study on real-time air pollution monitoring using wireless sensors on public transport vehicles.The study is part of the GreenIoT project in Sweden,which utilizes Internet-of-Things to measure air pollution level in the city center of Uppsala.Through deploying low-cost wireless sensors,it is possible to obtain more fine-grained and real-time air pollution levels at different locations.The sensors on public transport vehicles complement the readings from stationary sensors and the only ground level monitoring station in Uppsala.The paper describes the deployment of wireless sensors on Uppsala buses and the integration of the mobile sensor network with the GreenIoT testbed.Extensive experiments have been conducted to evaluate the communication quality and data quality of the system.

Short-term Effects of Air Pollution on Mortality in a Heavily Polluted Chinese City

Since the London fog in 1952, numerous epidemioLogical studies have revealed that both short-term and long-term exposure to air pollutants is associated with the development of diseases[1]. Up to date, the assessment of air quality on health and air quality standard establishment in developing countries were mainly relied on extrapolation based on the results from long-term cohort studies conducted in Europe and North America.

A Study on Air Pollution in Ulaanbaatar City, Mongolia

At present, air pollution has become the main problem in many developed and developing countries. Especially, in Ulaanbaatar city of Mongolia, it has become one of the most tackled issues of every citizen living in the capital city. The aim of this study is to highlight the trend of air pollution and pollution sources in the Mongolian capital and conduct some air pollution analyses. Overall, the study indicates that the air pollution in Ulaanbaatar city is a very serious problem and for its reduction, rapid and thorough measures should be taken.

Determination of Air Pollutant Concentrations in Plant Species in Relation to Pollution Sources

Air quality has been a major health issue in urban areas in recent decades. Human activities release a large number of pollutants into the atmosphere which has a direct impact on plant health and leads to ecosystem degradation. The objective of this study is to contribute to a better evaluation of the impact of the air quality of the city of Togo on biological resources. The determination of pollutants was done on samples of plant species with a strong link with the source of pollution. The determination of Sulfur dioxide (SO2) was done by the ripper method. The determination of carbon and estimation of CO2 and CO by the colorimetric method. The determination of nitrogen was done by the Kjeldhal method. The results showed that at the industrial level the amount of CO2 in Alternanthera repens is high with a value of 53.3911 mg/ml. On the other hand, the quantity of CO in Senna occidentalis is 44.3619 mg/ml. In Pithecellobium dulce, the quantity of SO2 and NO2 are evaluated respectively to 0.1588 mg/ml and 0.3696 mg/ml. Regarding to the dumps, the quantity of CO2 in Newbouldia laevis is very high with a value of 65.8508 mg/ml. On the other hand the amount of CO in Senna occidentalis is 51.6106 mg/ml. The quantity of SO2 in Newbouldia laevis is 0.2101 mg/ml and NO2 in Ocimum canum is 0.2744 mg/ml. At the level of roads, the quantities of CO2 and CO in Eragrostis tenella are very high with values respectively equal to 74.4092 mg/ml and 62.2654 mg/ml. On the other hand, the amount of NO2 in Amaranthus sp is 0.2304 mg/ml and that of SO2 in Eragrostis Tenella is 0.1691 mg/ml. The use of a plant bioindicator sensitive to pollutants, allowed concluding that the air of the city of Lome is polluted. The concentration of carbon dioxide and carbon monoxide is much more evident in return when the health of plant species is threatened.

Analysis of Micro Organic Compound Pollution in Major City River Reaches of the Main Stem of the Changjiang River

It has become general for surface waters being polluted by micro organic compounds. In order to know the current pollution situation and the properties of micro organic compounds in the Changjiang River, a test was performed on micro organic compounds in the water, bottom material and fish bodies which were sampled from major city river reaches of the Changjiang River. Based on the test result, researchers described and analyzed the sorts, concentration level and distribution features of micro organic compounds. A comprehensive evaluation was conducted by adopting the method of MEG (Multimedia Environmental Goals). The study indicated that ① the water body of major city river reaches of the Changjiang River has been generally polluted. In the test, totally 12 types with 308 kinds of organic compounds were detected. The main pollutants were paraffins, PAHs and lipids; and ② micro organic pollutant content in fish bodies was generally higher than that in bottom material which is in turn higher than that in water; and ③ pollution is relatively severe in the river reaches of mid-to-large comprehensive industrial cities with fairly great TAS (Total Ambient Severity) of public health and ecological system.

Thoughts on Energy Solutions to Alleviation of Air Pollution during the Heating Period in Key Areas

Since the implementation of the Action Plan for Air Pollution Prevention and Control , all regions of China have steadily promoted the prevention and control of air pollution and achieved results continuously. However, the atmospheric environment in key areas such as Beijing-Tianjin-Hebei region, the Yangtze River Delta region, and Fenwei Plain is still severe, and especially during the heating period heavy pollution occurs frequently, which has become the focus and difficulty of improving the quality of the atmospheric environment and is also the weakest link of China s air pollution control at present. How to alleviate air pollution, how to win the battle of pollution prevention and control, how to hold the fruits of the blue sky defense war, energy consumption is key.

AN AIR POLLUTION PREDICTION TECHNIQUE FOR URBAN DISTRICTS BASED ON MESO-SCALE NUMERICAL MODEL

Taking Shenzhen city as an example, the statistical and physical relationship between the density of pollutants and various atmospheric parameters are analyzed in detail, and a space-partitioned city air pollution potential prediction scheme is established based on it. The scheme considers quantitatively more than ten factors at the surface and planetary boundary layer (PBL), especially the effects of anisotropy of geographical environment, and treats wind direction as an independent impact factor. While the scheme treats the prediction equation respectively for different pollutants according to their differences in dilute properties, it considers as well the possible differences in dilute properties at different districts of the city under the same atmospheric condition, treating predictions respectively for different districts. Finally, the temporally and spatially high resolution predictions for the atmospheric factors are made with a high resolution numerical model, and further the space-partitioned and time-variational city pollution potential predictions are made. The scheme is objective and quantitative, and with clear physical meaning, so it is suitable to use in making high resolution air pollution predictions.

Comprehensive Review on Air Pollution Control Measures for Non-Attainment Cities of Uttar Pradesh, India

Introduction: The Indian state of Uttar Pradesh (UP) for the past many years has been reported to have many cities with highly polluted air quality. The state has been taking meticulous steps in combating air pollution in the form of action plans, introduced especially in its 17 non-attainment cities (NAC). To assess the progress and development of these action plans in UP, the present study has done an in-depth analysis and review of the state’s action plans and city micro action plans. Materials and Methods: In this research study, the analysis of the latest action plan reports, micro action plan reports as well as the recommendations for combating air pollution-related issues in the 17 NAC of the UP state has been well documented. Uttar Pradesh Pollution Control Board (UPPCB) has prepared these reports to highlight the progress of the plans in response to the growing air pollution in these cities. The information present in the reports has been used to further study sector-specific, category-specific action plans, institutional responsibility, and the present status of the action plans. Results: On average, the highest weightage in action plans was given to sector-specific categories such as Road dust and construction activities (24%). It was also observed that Urban local bodies (~50%) were majorly responsible to implement the action points and 56% of the action points were jointly implemented by multiple agencies.

GIS-based Analysis of Main Air Pollutants of Changchun City in Summer

To gain a better understanding of the characteristics of air pollution of Changchun city, P. R. China, in summer, the analytical methods of geographical information system（GIS） and statistical analysis software SPSS were applied to the analysis of the monitored concentrations of SO2 , NO2, and O3 in July of 2002 to 2004 in Changchun city. The results obtained show that the average hourly O3 concentrations in July 2002 and 2004 were higher than the firstlevel hourly standard of China. At the same time, the dynamic distribution of the 03 concentration and the relationship between the concentration of 03 and that of NOx were studied. The air quality evaluation result of Changchun city indicates that the southwest of this city was heavily polluted during the monitored period.

Analysis on the Variation of Urban Heat Island Intensity and Its Relationships with Air Pollutants in Kunming,China

[ Objective] The research aimed to study variation of the urban heat island （UHI） intensity and its relationships with air pollutants in Kunming. [ Method] Observation data of the air temperature series from Kunming station and surrounding Taihuashan meteorological station were used to investigate the variation characteristics of UHI intensity in Kunming during 2001 -2010. Meanwhile, relationship between UHI intensity and air quality on the basis of concentration data for primary air pollutants （SO2, NO2 and PMlo ） from environmental monitoring station of Kunming was studied in this paper. [Result] Annual mean UHI intensity increased at the rate of 0.110 ℃/10 a, and UHI intensity was higher in dry season than that in rainy season. The change trend of air pollutants level in Kunming was in accordance with that of the UHI intensity. When UHI intensity reached peak value, air pollutants showed high concentration. UHI intensity had significantly positive correlation with air pollutants level. Annual mean UHI intensity had significantly positive correlation with PMlo level under the likelihood of 95%, but monthly mean UHI intensity had significantly positive correlation with NO2 level under the likelihood of 99%. In addition, annual statistics days with Grade I air quality obviously decreased with the strengthenment of UHI intensity. Therefore, air quality in Kunming City tumed more deteriorated with the enhancement of UHI intensity. [ Conclusion] The research findings provided a valid scientific basis for the mitigation of UHI and improvement of the atmospheric environment in Kunming.

Diurnal and Seasonal Variation of BTX in Ambient air of One Urban Site in Carmen City, Campeche, Mexico

BTX (benzene, ethylbenzene, toluene and p-xylene) and meteorological parameters were measured in ambient air of an urban site located in Carmen City, Campeche, Mexico. A total of 412 samples were collected for BTX and analyzed by Gas Chromatography with Flame Ionization Detection (GC-FID). Meteorological parameters were measured by a portable station. A marked diurnal variation was found for all measured BTX. The highest concentrations occurred during midday (13:00-14:30 h). A clear seasonal pattern was observed too for all compounds registering their highest levels during summer sampling period. Mean concentrations for benzene, ethylbenzene, toluene and p-xylene were: 5.42, 3.97, 11.24 and 8.32 ppbv, respectively. BTX abundance showed the following order: toluene > p-xylene > benzene > ethylbenzene. BTX maximum concentrations were found when winds blowed from E and NE. Important oil industry sources and avenues are located at these directions. These sources could contribute to the levels of BTX in this site.

Asthma Hospital Admissions and Ambient Air Pollutant Concentrations in New York City

Air pollution is considered a risk factor for asthma. In this paper, we analyze the association between daily hospital admissions for asthma and ambient air pollution concentrations in four New York City counties. Negative binomial regression is used to model the association between daily asthma hospital admissions and ambient air pollution concentrations. Potential confounding factors such as heat index, day of week, holidays, yearly population changes, and seasonal and long-term trends are controlled for in the models. Nitrogen dioxide (NO2), sulfur dioxide (SO2) and carbon monoxide (CO) show the most consistent statistically significant associations with daily hospitalizations for asthma during the entire period (1996-2000). The associations are stronger for children (0 - 17 years) than for adults (18 - 64 years). Relative risks (RR) for the inter-quartile range (IQR) of same day 24-hour average pollutant concentration and asthma hospitalizations for children for the four county hospitalization totals were: NO2 (IQR = 0.011 ppm, RR = 1.017, 95% CI = 1.001, 1.034), SO2 (IQR = 0.008 ppm, RR = 1.023, 95% CI = 1.004, 1.042), CO (IQR = 0.232 ppm, RR = 1.014, 95% CI = 1.003, 1.025). In the case of ozone (O3) and particulate matter (PM2.5) statistically significant associations were found for daily one-hour maxima values and children’s asthma hospitalization in models that used lagged values for air pollution concentrations. Five-day weighted average lag models resulted in these estimates: O3 (one-hour maxima) (IQR = 0.025 ppm, RR = 1.049, 95% CI = 1.002, 1.098), PM2.5 (one-hour maxima) (IQR = 16.679 μg/m3, RR = 1.055, 95% CI = 1.008, 1.103). In addition, seasonal variations were also explored for PM2.5 and statistically significant associations with daily hospital admissions for asthma were found during the colder months (November-March) of the year. Important differences in pollution effects were found across pollutants, counties, and age groups. The results for PM2.5 suggest that the composition of PM is important to this health outcome, since the major sources of NYC PM differ between winter and summer months.

Elemental Analysis of Air Suspended Particles in Rasht City Using Instrumental Neutron Activation Analysis

Instrumental neutron activation analysis （INAA） has turned out to be particularly useful in the analysis of suspended particles. This work describes the INAA characterization of air particulate matter collected in Rasht city, Iran. The particulate matter was collected in two sampling sites for elemental analysis in the period of winter-summer 2009. Samples have been transferred to the environmental laboratory of radiation applications research school. Neutron exposures were performed in Tehran Research Reactor, and measurements were carried out using HPGe detectors coupled to gamma ray spectrometer. In these filter samples, the elements, AI, As, Ba, Br, Ca, CI, Co, Cr, Cu, Fe, Hg, K, La, Mg, Mn, Na, Sb, Sc, Ti, V and Zn were determined using different irradiation and counting protocols. Results show that mean concentration of most air-pollutants in Rasht are lower than those measured in other cities in Iran （Tehran, Shiraz, Isfahan）. Collected data were also compared with reported results for other cities in the world.

Air Quality Estimation Using Nonhomogeneous Markov Chains: A Case Study Comparing Two Rules Applied to Mexico City Data

A nonhomogeneous Markov chain is applied to the study of the air quality classification in Mexico City when the so-called criterion pollutants are used. We consider the indices associated with air quality using two regulations where different ways of classification are taken into account. Parameters of the model are the initial and transition probabilities of the chain. They are estimated under the Bayesian point of view through samples generated directly from the corresponding posterior distributions. Using the estimated parameters, the probability of having an air quality index in a given hour of the day is obtained.

Preliminary Results of Application Mobilev Traffic model to Calculate Air Emission and Assessing Policies for Reducing Air Emissions in an ASEAN City

Traffic is one of the main air pollution source in urban cities,especially in Ho Chi Minh City.Annually,it emits a huge amount of pollutants into the atmosphere;and air quality in HCMC becomes worse due to circulation of outdate private vehicles.Therefore,clean air plan(CAP)is necessary for reducing air pollution level in the city and air emission inventory(EI)is an essential step to develop CAP.Mobilev model from Germany was chosen to conduct EI for HCMC.Objectives include of this study:(i)elaborating an air emission inventory(EI)from road traffic activities over HCMC;and(ii)assessing emission control policies and study abatement strategies to reduce air pollution level from traffic activities for HCMC in 2020.The results indicated that motorcycles are the main sources of air emission in HCMC.The emissions of CO are 3,586.707 tons/year,following ish VOC,NOx,CH4,NO2,SO2 and particulate matter(PM).In addition,CO2,which is one of the main greenhouse gases,also included and contributed 36,293.501 ton/year.These pollutants concentrated in the center which has crowded roads and population,affecting directly human health.Therefore,a replaced private vehicle with public transportation is necessary to reduce emissions.Two abatement strategies to 2020 for reducing emissions were performed and showed that if the HCMC government has severe policies on motor vehicles,the emission will be reduced until 60%,opposite emissions in 2020 will be increased to 200%.

Harmful Interaction of Exhaust Gas on Contaminating Air in City Agglomeration

The article presents results inducing pollution of the air which is through traffic automotive vehicles. of research of diesel engines working the exhaust gas smokiness against its in city agglomeration working general pollution in two urban Also the study of the factors agglomerations with similar