Mapping Air Quality Using Remote Sensing Technology: A Case Study of Nairobi County

Nairobi County experiences rapid industrialization and urbanization that contributes to the deteriorating state of air quality, posing a potential health risk to its growing population. Currently, in Nairobi County, most air quality monitoring stations use low-cost, inaccurate monitors prone to defects. The study’s objective was to map Nairobi County’s air quality using freely available remotely sensed imagery. The Air Pollution Index (API) formula was used to characterize the air quality from cloud-free Landsat satellite images i.e., Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI from Google Earth Engine. The API values were computed based on vegetation indices namely NDVI, TVI, DVI, and the SWIR1 and NIR bands on the QGIS platform. Qualitative accuracy assessment was done using sample points drawn from residential, industrial, green spaces, and traffic hotspot categories, based on a passive-random sampling technique. In this study, Landsat 5 API imagery for 2010 provided a reliable representation of local conditions but indicated significant pollution in green spaces, with recorded values ranging from -143 to 334. The study found that Landsat 7 API imagery in 2002 showed expected results with the range of values being -55 to 287, while Landsat 8 indicated high pollution levels in Nairobi. The results emphasized the importance of air quality factors in API calibration and the unmatched spatial coverage of satellite observations over ground-based monitoring techniques. The study recommends the recalibration of the API formula for characteristic regions, exploring newer satellite sensors like those onboard Landsat 9 and Sentinel 2, and involving key stakeholders in a discourse to develop a suitable Kenyan air quality index.

Spatial-temporal Analysis of Daily Air Quality Index in the Yangtze River Delta Region of China During 2014 and 2016

Urban air pollution is a prominent problem related to the urban development in China, especially in the densely populated urban agglomerations. Therefore, scientific examination of regional variation of air quality and its dominant factors is of great importance to regional environmental management. In contrast to traditional air pollution researches which only concentrate on a single year or a single pollutant, this paper analyses spatiotemporal patterns and determinants of air quality in disparate regions based on the air quality index(AQI) of the Yangtze River Delta region(YRD) of China from 2014 to 2016. Results show that the annual average value of the AQI in the YRD region decreases from 2014 to 2016 and exhibit a basic characteristic of ‘higher in winter, lower in summer and slightly high in spring and autumn'. The attainment rate of the AQI shows an apparently spatial stratified heterogeneity, Hefei metropolitan area and Nanjing metropolitan area keeping the worst air quality. The frequency of air pollution occurring in large regions was gradually decreasing during the study period. Drawing from entropy method analysis, industrialization and urbanization represented by per capita GDP and total energy consumption were the most important factors. Furthermore, population agglomeration is a factor that cannot be ignored especially in some mega-cities. Limited to data collection, more research is needed to gain insight into the spatiotemporal pattern and influence mechanism in the future.

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.

Particulate Matter-Based Air Quality Index Estimate for Abuja, Nigeria: Implications for Health

In recent years, urban air quality in developing countries such as Nigeria has continued to degenerate and this has constituted a major environmental risk to human health. It has been shown that an increase in ambient particulate matter (PM10) load of 10 μg/m3 reduces life expectancy by 0.64 years. Air Quality Index (AQI) as demonstrated in this study shows how relatively clean or polluted the boundary layer environment of any location can be. The study was designed to measure the level of suspended particulate matter (PM2.5 and PM10) for dry and wet seasons, compute the prevalent air quality index of selected locations in Abuja with possible health implications. Suspended particulate matter (PM2.5 and PM10) was assessed using handheld aerosol particulate sampler. The US Oak Ridge National AQI was adopted for the eleven (11) locations sampled and monitored. The study results showed that the air quality of the selected areas in Abuja were generally good and healthy. Dry season, assessments, showed 15 - 95 μg/m3 and 12 - 80 μg/m3 for PM2.5 and PM10, respectively. While in wet season, 09 - 75 μg/m3 and 07 - 65 μg/m3 were recorded for PM2.5 and PM10. However at Jebi Central Motor Park, there was light air contamination with AQI of 42 for dry season and 31 for wet season. Other locations had clean air with AQI ≤ 11. It is revealed that clean air exists generally during the wet season. Comparing study outcome to other cities in Nigeria, residents of Abuja are likely not to be affected with health hazards of particulate matter pollution. Nonetheless, the high range of PM2.5 and PM10 (fine and coarse particles) ratio evaluated i.e., 1.06 - 1.79 was higher than the WHO recommended standard of 0.5 - 0.8. This ratio remains a health concerns for sensitive inhabitants like pregnant women and their foetus as well as infants below age five whose respiratory airways are noted to have high surface areas and absorption capacity for fine particulate matter. Vegetation known to absorb suspended particulate matter should be planted across Abuja metropolitan areas and air quality monitoring stations installed at strategic locations for continuous monitoring and evaluations.

Ambient Air Quality Surveillance and Indexing in and around Mining Clusters in Western Kachchh Region, Gujarat, India

Generation of baseline information about ambient air quality of any given region assumes significance, when the area is 1) an active mine site, 2) proposed to be mined out in future, and 3) industrialization in the area is in fast pace. Ambient air quality monitoring (with respect to SPM, RPM, SO2, NOx and CO) was carried out in and around two mining complexes in western parts of Kachchh district in Gujarat to generate baseline air quality status of the area. This area has two major mine complexes and various large scale industrial projects (thermal power plants, cement plants and several ports and jetties) are also in pipeline. Ambient air sampling was carried out in eight locations within five km radial distance from two major mine sites, i.e. Panandhro and Mata-na-Madh, with four locations for each mine site. Air Quality Indexing was done for all the locations, since it is a simplest way for the prediction of ambient air quality status of any region with respect to industrial, residential and rural areas. Of the eight locations studied the air quality for six locations fell under fairly clean (Light Air Pollution, AQI 25-50) category, while the rest (rural areas in the region), had relatively better air quality and fell under clean (Clean Air, AQI 10-25) category.

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.

COVID19: Forecasting Air Quality Index and Particulate Matter (PM2.5)

Urbanization affects the quality of the air,which has drastically degraded in the past decades.Air quality level is determined by measures of several air pollutant concentrations.To create awareness among people,an automation system that forecasts the quality is needed.The COVID-19 pandemic and the restrictions it has imposed on anthropogenic activities have resulted in a drop in air pollution in various cities in India.The overall air quality index(AQI)at any particular time is given as the maximum band for any pollutant.PM2.5 is a fine particulate matter of a size less than 2.5 micrometers,the inhalation of which causes adverse effects in people suffering from acute respiratory syndrome and other cardiovascular diseases.PM2.5 is a crucial factor in deciding the overall AQI.The proposed forecasting model is designed to predict the annual PM2.5 and AQI.The forecasting models are designed using Seasonal Autoregressive Integrated Moving Average and Facebook’s Prophet Library through optimal hyperparameters for better prediction.An AQI category classification model is also presented using classical machine learning techniques.The experimental results confirm the substantial improvement in air quality and greater reduction in PM2.5 due to the lockdown imposed during the COVID-19 crisis.

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.

Spatio-temporal Characteristics and Geographical Determinants of Air Quality in Cities at the Prefecture Level and Above in China

In recent years, the large scale and frequency of severe air pollution in China has become an important consideration in the construction of livable cities and the physical and mental health of urban residents. Based on the 2016-year urban air quality index(AQI) data published by the Ministry of Environmental Protection of China, this study analyzed the spatial and temporal characteristics of air quality and its influencing factors in 338 urban units nationwide. The analysis provides an effective scientific basis for formulating national air pollution control measures. Four key results are shown. 1) Generally, air quality in the 338 cities is poor, and the average annual values for urban AQI and air pollution in 2016 were 79.58% and 21.22%, respectively. 2) The air quality index presents seasonal changes, with winter > spring > autumn > summer and a u-shaped trend. 3) The spatial distribution of the urban air quality index shows clear north-south characteristic differences and a spatial agglomeration effect; the high value area of air pollution is mainly concentrated in the North China Plain and Xinjiang Uygur Autonomous Region. 4) An evaluation of the spatial econometric model shows that differences in urban air quality are due to social, economic, and natural factors.

Spatial and Temporal Variation of Urban Air Quality: A GIS Approach

This study investigated the seasonal variation of ambient air quality status of Burdwan town using GIS approach. Concentration of SOR2R (sulphur dioxide), NOR2R (nitrogen dioxide) and RSPM (respiratory suspended particulate matter) were measured once a week for 24 hour in both premonsoon and postmonsoon season. The seasonal average concentration of the RSPM, SOR2R and NOR2R in premonsoon season was observed to be 188.56 ± 88.63, 5.12 ± 6.27 and 92.51 ± 64.78 mg/mP3P respectively whereas in postmonsoon it was 53.03 ± 38.27, 8.51 ± 7.11 and 162.85 ± 184.80 mg/mP3P respectively. Statistical analysis showed the significant monsoonal effect on mean difference of RSPM, SOR2R and NOR2R concentration. Postmonsoon concentration of ambient SOR2R and NOR2R were observed to be higher than premonsoon, suggesting longer residence times of these pollutants in the atmosphere due to stagnant conditions and low mixing height. Spatial distribution of pollutants throughout the town in both the season was represented by digital elevation model (DEM). On the basis of Air Quality Index (AQI) a GIS based air pollution surface models were generated in both the seasons by means of Inverse Distance Interpolation (IDINT) technique. From the output surface model it was found that in comparison to premonsoon there was a significant increase of clean and fairly clean area and decrease of moderately polluted area of the town during postmonsoon.

Evaluation of ambient air quality in Guangzhou, China

根据报导空气质量索引( API )和空气， pollutantmonitoring 数据由在最后 twenty-fiveyears ，空气质量的特征，突出的污染物质，和 SO2 的平均 annualconcentrations 的变化上监视车站的广州环境提供了， NO2 ，全部的推迟的微粒( TSP )，好微粒(PM_( 10 ))，广州城市里的公司 anddustfall 是显示出的 analyzed.Results 那 TSP 是在广州 City.Of 的 theambient

Analysis on Meteorological Factors of Air Quality and Health Strategy in Shiyan City

Based on daily newspaper of urban air quality and meteorological monitoring data in Shiyan City during 2014-2015,air pollution characteristics of industrialized city were studied,and change characteristics of air quality and impact factors were analyzed by combining weather data. Results showed that air quality of Shiyan City was dominated by grade-Ⅱand grade-Ⅲ weather,in which occurrence days of grade-Ⅱ weather accounted for 64.9% of statistical days,while grade-Ⅲ weather accounted for 17. 9%; air quality had obvious seasonal characteristics,and winter air quality was the worst,with AQI of 114. 1,while summer air quality was the best,with AQI of 70.6; primary pollutant was PM_(2.5),and annual average PM_(2.5),PM_(10) and AQI indexes were 0.059 μg/m^3,0.093 μg/m^3 and 85. 618; PM_(2.5),PM_(10) and AQI indexes were negatively correlated with temperature,water vapor pressure,low cloud amount,sunshine,wind velocity,rainfall,and were positively correlated with air pressure,total cloud amount,fog and haze.

Air Quality Indices, Sources and Impact on Human Health of PM₁₀and PM_2.5in Alexandria Governorate, Egypt

In this study, PM10 and PM2.5 were measured in seven sites representing different activities (the same sites of EEAA monitoring stations) in addition to eighth site that used as a background. All results were higher than AQLs of EEAA, US/EPA, and EC although PM10 and PM2.5 are considered to be a direct cause of cardiovascular diseases as well as lead to death and it may be a reason for a number of chest diseases in short-term as well as long-term. Results were compared to the Air Quality Forecast system which developed by EEAA and AQI which created by US/EPA was calculated for some PM10 and PM2.5. Probable potential anthropogenic sources for such high concentrations of PM included unpaved roads, indiscriminate demolition and construction work, industrial activities, and solid wastes. This study resulted in a number of suggestions and recommendations include: 1) Implementation of integrated ISO 26000 and ISO 14001, 2) EIMP/EEAA monitoring stations need restructuring plan to cover all areas in Alexandria, 3) EIMP/EEAA must be supported with PM2.5 monitors, 4) PM control systems must be used in all industrial activities to reduce PM pollution from the source, 5) AQL of PM2.5 in the ambient environment must be reduced and it must be included in the working environment parameters, 6) Environmental law must be applied strictly, and 7) Multidisciplinary co-operation especially between environment and public health specialists must be increased.

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.

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.

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.

Urbanisation Footprints and the Distribution of Air Quality in Nairobi City,Kenya

Various postulations on the relationship between urban morphology and air quality are qualitative.This fails to establish the strength of the contributions of each morphological parameter in the spatial distribution of the air quality.It is this gap in knowledge that this study sought to fill by modelling the correlation existing between the urban morphological variables of development density,land uses,biomass index and air quality values of Nairobi city.While 30 development zones of the city constituted the target population,IKONOS satellite imagery of the city for the year 2015 was utilised in establishing the development densities,land uses and biomass index.The parameters were transformed into numerical surrogates ranging from 1 to 10 with lower values accorded to zones with low biomass index,the highest development density,noxious land uses,high gaseous concentrations and vice-versa.Pearson’s correlation coefficients(r),coefficients of determination(R),t-tests and the Analysis of Variance(F-tests)with levels of significance being 95%were used to determine the strengths,significances and consistencies of the established relationships.The study established that development density is the most significant morphological variable influencing the distribution of air quality.This is followed by biomass index and to a weaker extent,land uses.

基于MLP和SARIMA的青岛市AQI预报模型

为掌握青岛市空气质量变化特征,为空气质量管控提供参考,以2014—2021年青岛市空气质量指数月统计历史数据为基础,通过深度学习算法中的多层神经网络建立了AQI与PM_(2.5)等6个主要污染物的预报模型,对青岛市空气质量的影响因素进行研究,并基于SARIMA模型预测了各污染物的浓度值,结合污染物浓度预测值和预报模型对AQI值进行了预测。根据预测结果,给出了改善青岛市空气质量的建议。

基于多源大数据的长三角城市群AQI遥感估算

长三角城市群的大气污染情况备受关注,本研究基于哨兵五号(Sentinel-5P)卫星遥感数据、地面大气污染国控点监测数据、气象数据、道路交通图层等多源大数据,首先定量获取研究区O_(3)、CO、NO_(2)、SO_(2)等要素的污染情况,并基于XGBoost机器学习模型定量估算研究区的大气颗粒物浓度,最后基于所有大气污染要素选择模型综合评判研究区空气质量指数(AQI)。本研究建立的大气污染各要素的遥感估算方法及AQI的综合评判方法可为区域大气污染的评定提供方法上的支持,并服务于大气污染综合治理、人民生产生活指引及健康防护等诸多方面。