Time Series Analysis and Forecasting of the Air Quality Index of Atmospheric Air Pollutants in Zahleh, Lebanon

During the last decades, air pollution has become a serious environmental hazard. Its impact on public health and safety, as well as on the ecosystem, has been dramatic. Forecasting the levels of air pollution to maintain the climatic conditions and environmental protection becomes crucial for government authorities to develop strategies for the prevention of pollution. This study aims to evaluate the atmospheric air pollution of the city of Zahleh located in the geographic zone of Bekaa. The study aims to determine a relationship between variations in ambient particulate concentrations during a short time. The data was collected from June 2017 to June 2018. In order to predict the Air Quality Index (AQI), Naïve, Exponential Smoothing, TBATS (a forecasting method to model time series data), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models were implemented. The performance of these models for predicting air quality is measured using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Relative Error (RE). SARIMA model is the most accurate in prediction of AQI (RMSE = 38.04, MAE = 22.52 and RE = 0.16). The results reveal that SARIMA can be applied to cities like Zahleh to assess the level of air pollution and to prevent harmful impacts on health. Furthermore, the authorities responsible for controlling the air quality may use this model to measure the level of air pollution in the nearest future and establish a mechanism to identify the high peaks of air pollution.

Exploring the Relationship between Spatiotemporal Variations in Air Quality and Meteorological Parameters before and during the COVID-19 Pandemic in Xi’an

The COVID-19 pandemic has significantly changed the air pollution of the world. The present study investigated the temporal and spatial variability in air quality in Xi’an, China, and its relationship with meteorological parameters during and before the COVID-19 pandemic. The outcomes of this study indicated that air pollutants, PM2.5, NO2, PM10, CO, and SO2 are likely to decrease during winter (25%, 50%, 30%, 40%, and 35%) to spring (30%, 55%, 38%, 50%, and 40%) and summer (40%, 58%, 60%, 55%, and 47%), respectively. However, the concentration of O3-8h increased by 40%, 55%, and 65% during winter, spring, and summer, respectively. The values of the air quality index decreased during the COVID-19 period. Furthermore, significant positive trends were reported in PM2.5, NO2, PM10, O3, and SO2, and no notable trends in CO during the COVID-19 pandemic. Both during and before the COVID-19 period, PM10, NO2, PM2.5, CO, and SO2 showed a negative correlation with the temperature and a moderately positive significant correlation between O3-8h and temperature. The findings of this study would help understand the air pollution circumstances in Xi’an before and during the COVID-19 period and offer helpful information regarding the implications of different air pollution control strategies.

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.

Unveiling the Predictive Capabilities of Machine Learning in Air Quality Data Analysis: A Comparative Evaluation of Different Regression Models

Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.

Short-Term Air Quality Gains of COVID-19 Pandemic Lockdown of Port Harcourt, Nigeria

The air quality index (AQI) of a location informs how clean or unhealthy the ambient air is. While COVID-19 pandemic on one hand threatened the health of mankind globally, on the other hand was a respite to poor air quality of most cities. This study evaluated the positive effects of the brief COVID-19 lockdown on the air quality of Port Harcourt city, Nigeria. Air quality parameters aimed at assessing air quality index of Port Harcourt Metropolis before, during and after COVID-19 pandemic lockdown were monitored and compared. Data were analysed and AQI of sampled locations computed using the US EPA recommended standard procedure. Results from the study showed that, the ambient air quality of Port Harcourt was hazardous for breathing before lockdown. During shutdown of activities, the air quality improved to unhealthy status, with an average reduction AQI of 261.7 points. However, an average increase of 100.7 points, resulting to very unhealthy air status for residents after lockdown was observed. The unhealthy status during lockdown shows that anthropogenic activities were still on despite the Pandemic shutdown of economic activities. Also, decrease in levels of the criteria air pollutants was observed. Before lock down, the range levels of SO3, NO2, CO, O3, PM2.5 and PM10 were <0.1 - 1.2 ppm, <0.1 - 0.1 ppm, 8 - 28 ppm, <0.1 ppm, 20 - 140 μg/m3, 15 - 135 μg/m3, respectively. In the period of lockdown, the levels reduced considerably, especially CO and PM2.5 and PM10 (1 - 12 ppm, 5 - 60 μg/m3, and 10 - 50 μg/ m3). Conversely, after lockdown, there was upsurge in levels of the pollutants, especially CO and PM2.5 and PM10 (4 - 16 ppm, 10 - 110 μg/m3, 10 - 90 μg/m3). Authorities are expected to establish routine air quality measurements stations and communicate daily air quality to residents, for public health precaution purposes. Shutdown of industrial activities instituted by Government in curtailing the surge of COVID-19 pandemic could likely be a novel environmental model for mitigating air pollution in highly hazardous air pollution emergency domains.

Role of Atmospheric Boundary Layer(ABL)Height and Ventilation Coefficient on Urban Air Quality--A study based on Observations and NWP Model

Air pollution is an issue of great concern in any urban region due to its serious health implications.The capital of India,New Delhi continues to be in the list of most polluted cities since 2014.The air quality of any region depends on the ability of dispersion of air pollutants.The height or depth of the atmospheric boundary layer(ABL)is one measure of dispersion of air pollutants.Ventilation coefficient is another crucial parameter in determining the air quality of any region.Both of these parameters are obtained over Delhi from the operational global numerical weather prediction(NWP)model of National Centre for Medium Range Weather forecasting(NCMRWF)known as NCMRWF Unified Model(NCUM).The height of ABL over Delhi,is also obtained from radiosonde observations using the parcel method.A good agreement is found between the observed and predicted values of ABL height.The maximum height of ABL is obtained during summer season and minimum is obtained in winter season.High values of air pollutants are found when the values of ABL height and ventilation coefficient are low.

Air Pollution Exposure Based on Nighttime Light Remote Sensing and Multi-source Geographic Data in Beijing

Air pollution is a problem that directly affects human health,the global environment and the climate.The air quality index(AQI)indicates the degree of air pollution and effect on human health;however,when assessing air pollution only based on AQI monitoring data the fact that the same degree of air pollution is more harmful in more densely populated areas is ignored.In the present study,multi-source data were combined to map the distribution of the AQI and population data,and the analyze their pollution population exposure of Beijing in 2018 was analyzed.Machine learning based on the random forest algorithm was adopted to calculate the monthly average AQI of Beijing in 2018.Using Luojia-1 nighttime light remote sensing data,population statistics data,the population of Beijing in 2018 and point of interest data,the distribution of the permanent population in Beijing was estimated with a high precision of 200 m×200 m.Based on the spatialization results of the AQI and population of Beijing,the air pollution exposure levels in various parts of Beijing were calculated using the population-weighted pollution exposure level(PWEL)formula.The results show that the southern region of Beijing had a more serious level of air pollution,while the northern region was less polluted.At the same time,the population was found to agglomerate mainly in the central city and the peripheric areas thereof.In the present study,the exposure of different districts and towns in Beijing to pollution was analyzed,based on high resolution population spatialization data,it could take the pollution exposure issue down to each individual town.And we found that towns with higher exposure such as Yongshun Town,Shahe Town and Liyuan Town were all found to have a population of over 200000 which was much higher than the median population of townships of51741 in Beijing.Additionally,the change trend of air pollution exposure levels in various regions of Beijing in 2018 was almost the same,with the peak value being in winter and the lowest value being in summer.The exposure intensity in population clusters was relatively high.To reduce the level and intensity of pollution exposure,relevant departments should strengthen the governance of areas with high AQI,and pay particular attention to population clusters.

Evaluation of Ambient Air Quality and Its Changing Trend in Jinan City during 2013-2020

Based on the automatic monitoring data of ambient air in Jinan City from 2013 to 2020,the changing trend and characteristics of air quality in Jinan City during 2013-2020 were analyzed by using the fuzzy comprehensive evaluation,air quality index(AQI)and ambient air quality comprehensive index methods.The three methods are different in principle,purpose of use,and characterization methods,but the conclusions are consistent.The ambient air quality in Jinan City was improved significantly from 2013 to 2020.The prime pollutants were mainly PM_(2.5)and PM_(10),but the impact on air quality declined,and the impact of O_(3)on air quality increased.The complex pollution characteristics were obvious.Air pollution was the most severe in winter and lighter in summer.

基于机器感知与学习的空气颗粒物智能检测、识别与预警方法研究综述

随着空气污染问题的不断加剧,准确检测和及时预警空气颗粒物(particulate matter,PM)的重要性日益突出。传统方法依赖专业设备,不适用于实时检测。与传统方法相比,基于机器感知与学习的方法体现出技术优势,具有可实时检测、准确性高等优点。因此,对近几年的基于机器感知与学习的PM智能检测、识别与预警方法进行详细综述。首先,对PM的标准和来源进行介绍;然后,从检测、识别和预警这3个方面详细总结了各类方法,并对比各方法的特点和性能,其中,基于机器学习和深度学习的方法在各研究中取得了较大进展;最后,总结全文主要内容,并提出当前领域面临的挑战以及未来的重点研究方向。未来的研究应该继续关注技术创新和数据质量,以实现更好的空气质量监测和管理。

Financial depth or breadth:What really matters for fighting air pollution in China?

China's rapid economic development leads to a series of environmental problems in the long run,such as air pollution.Environmental pollution has become a bottleneck restricting the sustainable development of China's economy.As such,pollution has become a key issue for China as让tries to continuously improve environmental quality and establish a harmonious coexistence between man and nature.This paper uses spatial econometric analysis to empirically test the existence of the Environmental Kuznets Curve(EKC)in China while also examining the impact of financial development on its inflection point by applying Air Quality Index and PM_(2.5) data of 283 prefecture-level cities in China from 2015 to 2017.Findings from this study indicate that the EKC of air pollution in the whole country presents an inverted U-shape based on both the traditional and new EKC models.After testing the sub-sample in different areas,the EKC still presents an inverted U-shape based on the new EKC model in the eastern and central areas,though not in the western area.In considering the moderating role of financial development based on the new EKC model,we find that the increase of financial depth will cause the EKC inflection point to shift to the left on the national scale and in the eastern region,while the effect of the financial breadth will be largely insignificant.With regard to the central area,both the breadth and the depth of financial development will significantly shift the inflection point to the right,delaying the arrival of the EKC inflection point.Therefore,the local authority of each area should formulate differentiated financial development policies to promote the early arrival of the EKC inflection point.

Multi-Site Air Pollutant Prediction Using Long Short Term Memory

The current pandemic highlights the significance and impact of air pollution on individuals. When it comes to climate sustainability, air pollution is amajor challenge. Because of the distinctive nature, unpredictability, and greatchangeability in the reality of toxins and particulates, detecting air quality is apuzzling task. Simultaneously, the ability to predict or classify and monitor airquality is becoming increasingly important, particularly in urban areas, due tothe well documented negative impact of air pollution on resident’s health andthe environment. To better comprehend the current condition of air quality, thisresearch proposes predicting air pollution levels from real-time data. This studyproposes the use of deep learning techniques to forecast air pollution levels.Layers, activation functions, and a number of epochs were used to create the suggested Long Short-Term Memory (LSTM) network based neural layer design. Theuse of proposed Deep Learning as a structure for high-accuracy air quality prediction is investigated in this research and obtained better accuracy of nearly 82% compared to earlier records. Determining the Air Quality Index (AQI) and danger levelswould assist the government in finding appropriate ways to authorize approaches toreduce pollutants and keep inhabitants informed about the findings.

A supplementary assessment system of AQI-V for comprehensive management and control of air quality in chemical industrial parks

Volatile organic compounds(VOCs)are the dominant pollutants in industrial parks.However,they are not generally considered as part of the air quality index(AQI)system,which leads to a biased assessment of pollution in industrial parks.In this study,a supplementary assessment system of AQI-V was established by analyzing VOCs characteristics with vehicle-mounted PTR-TOFMS instrument,correlation analysis and the standards analysis.Three hourly and daily scenarios were considered,and the hierarchical parameter setting was further optimized by field application.The hourly and daily assessments revealed the evaluation factors for the discriminability of different air quality levels,practiced value for regional air quality improvement,and the reservation of general dominant pollutants.Finally,the universality testing in ZPIP successfully recognized most of the peaks,with 54.76%,38.39%and 6.85%for O_(3),VOCs and NO_(2) as the dominant pollutant,and reflected the daily ambient air quality condition,togetherwith the dominant pollutant.The AQI-V systemwith VOCs sub-index is essential for air quality evaluation in industrial parks,which can further provide scientific support to control the pollution of VOCs and the secondary pollutant,therefore significantly improve the air quality in local industrial parks.

Long-term variation and evaluation of air quality across Hong Kong region

Study of Air Quality Objectives(AQOs)and long-term changes of air pollution plays a decisive role in formulating and refining pollution control strategies.In this study,10-year variations of six major air pollutants were analyzed at seven monitoring sites in Hong Kong region.The continuous decrease of annual averaged concentrations of NO_(2),SO_(2),CO,PM_(2.5)and PM_(10)and numbers of days with severe pollution conditions validated the efficiency of the series of air pollution control schemes implemented by the Hong Kong region government.However,there is still a big gap to meet the ultimate targets described by the World Health Organization.Besides,the concentration of O_(3)at roadside and urban stations increased by 135%±25%and 37%±18%from 2011 to 2020,respectively,meanwhile the highest 8 hr averaged O_(3)concentration was observed as 294μg/m^(3)at background station in 2020,which pointed out the increasing ozone pollution in Hong Kong region.There was a great decrease in the annual times of air quality health index(AQHI)laying in“high”,“very high”and“serious”categories from 2011 to 2020 with the decrease rate of 89.70%,91.30%and 89.74%at roadside stations,and 79.03%,95.98%and 72.73%at urban stations,respectively.Nevertheless,the number of days categorized as“high”or above at roadside station was twice more than that in the urban station during the past ten years.Thus,more policies and attentions should be given to the roadside air quality and its adverse health effect to pedestrians on street.

Characteristics and Cause Analysis of Heavy Haze in Changchun City in Northeast China

Northeast China has been reported as having serious air pollution in China with increasing occurrences of severe haze epi- sodes. Changchun City, as the center of Northeast China, has longstanding industry and is an important agricultural base. Additionally, Changchun City has a long winter requiring heating of buildings emitting pollution into the air. These factors contribute to the complex- ity of haze pollution in this area. In order to analyze the causes of heavy haze, surface air quality has been monitored from 2013 to 2015. By using satellite and meteorological data, atmospheric pollution status, spatio-temporal variations and formation have been analyzed. Results indicated that the air quality in 88.9% of days exceeding air quality index （AQI） level-1 standard （AQI 〉50） according to the National Ambient Air Quality Standard （NAAQS） of China. Conversely, 33.7% of the days showed a higher level with AQI 〉 100. Ex- treme haze events （AQI 〉 300） occurred frequently during agricultural harvesting period （from October 10 to November 10）, intensive winter heating period （from Late-December to February） and period of spring windblown dust （April and May）. Most daily concentra- tions of gaseous pollutants, i.e., NO2 （43.8 gg/m3）, CO （0.9 mg/m3）, SO2 （37.9 gg/m3）, and 03 （74.9 gg/m3） were evaluated within level-1 concentration limits of NAAQS standards. However, particulate matter （PM2.5 and PMI0） concentrations （67.3 ~tg/m3and 115.2 ~g/m3, respectively） were significantly higher than their level-1 limits. Severe haze in spring was caused by offsite transported dust and windblown surface soil. Heavy haze periods during fall and winter were mainly formed by intensive emissions of atmospheric pollutants and steady weather conditions （i.e., low wind speed and inversion layer）. The overlay emissions of widespread straw burning and coal combustion for heating were the dominant factors contributing to haze in autumn, while intensive coal burning during the coldest time was the primary component of total emissions. In addition, general emissions including automobile exhaust, road and construction dust, residential and industrial activities, have significantly increased in recent years, making heavy haze a more frequent occurrence. There- fore, both improved technological strategies and optimized pollution management on a regional scale are necessary to minimize emis- sions in specified seasons in Changchun City, as well as comprehensive control measures in Northeast China.

Indoor Particulate Matter Assessment in a Northern Nigerian Abattoir and a Residential Building

Indoor air pollution in buildings puts people at risk of developing respiratory and cardiovascular diseases.Particulate matter(PM)exposure is known to cause these health issues.Preliminary efforts were made in this study to assess the quantity and quality of PM1.0,PM_(2.5),and PM_(10)present in an abattoir and a residential building in northern Nigeria.Canree A1 low-cost sensor was used to monitor the locations,8 hourly for two weeks.The results showed that the aver­age values(μg/m^(3))of PM1.0,PM_(2.5),and PM_(10)in an abattoir were 62.74,161.94,and 199.08,respectively,and in a residential building were 28.70,83.31,and 103.71.The average Air Quality Index(AQI)of the abattoir office was Very Unhealthy,while the living room of the residential building was unhealthy.The PM_(2.5),and PM_(10)levels were higher than the international(WHO)and national(FMEnv)standard limits,indicating a potential danger to building occupants.It is expected that the indoor environment of the locations will be improved by the use of good ventilators(adequate windows and doors)and the provision of good extractors.

A demonstration project of Global Alliance against Chronic Respiratory Diseases:Prediction of interactions between air pollution and allergen exposure—the Mobile Airways Sentinel NetworK-Impact of air POLLution on Asthma and Rhinitis approach

This review analyzes the state and recent progress in the field of information support for pollen allergy sufferers.For decades,information available for the patients and allergologists consisted of pollen counts,which are vital but insufficient.New technology paves the way to substantial increase in amount and diversity of the data.This paper reviews old and newly suggested methods to predict pollen and air pollutant concentrations in the air and proposes an allergy risk concept,which combines the pollen and pollution information and transforms it into a qualitative risk index.This new index is available in an app(Mobile Airways Sentinel NetworK-air)that was developed in the frame of the European Union grant Impact of Air POLLution on sleep,Asthma and Rhinitis(a project of European Institute of Innovation and Technology-Health).On-going transformation of the pollen allergy information support is based on new technological solutions for pollen and air quality monitoring and predictions.The new information-technology and artificial-intelligence-based solutions help to convert this information into easy-to-use services for both medical practitioners and allergy sufferers.

Mortality weighting-based method for aggregate urban air risk assessment

This paper deals with a mortality-weighted synthetic evaluation （MWSE） method for evaluating urban air risk. Sulphur dioxide （SO2）, nitrogen oxide （NOx）, and particulate matter （PMl0） were used as pollution indices. The urban area of Hangzhou, China is divided into 756 grid cells, with a resolution of 1 km× 1 km, and is evaluated using the MWSE and the air quality index （AQI）, a widely-used method to evaluate ambient air quality and air risk. In an evaluation of one day in April 2004, the surface areas categorized as levels Ⅰ and Ⅲ, as defined by the integrated air risk evaluation, were 27.3% and 3.3% lower, respectively, than grades Ⅰ and Ⅲ defined by the AQI evaluation. Meanwhile, the areas classified as level Ⅱ or above level Ⅲ by the integrated air risk evaluation were 55.1% and 101. 1% higher, respectively, than grade Ⅱ or above grade Ⅲ when using the AQI evaluation. From this comparison, we find that the MWSE method is more sensitive than the AQI method. The AQI method uses a single index to assess integrated air quality and is therefore unable to evaluate integrated air risks due to multiple pollutants. The MWSE method overcomes this problem, providing improved accuracy in air risk assessment.