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.

The Establishment of a New Air Health Index Integrating the Mortality Risks Due to Ambient Air Pollution and Non-Optimum Temperature

A composite Air Health Index(AHI)is helpful for separately emphasizing the health risks of multiple stimuli and communicating the overall risks of an adverse atmospheric environment to the public.We aimed to establish a new AHI by integrating daily mortality risks due to air pollution with those due to non-optimum temperature in China.Based on the exposure-response(E-R)coefficients obtained from time-series models,the new AHI was constructed as the sum of excess mortality risk associated with air pollutants and non-optimum temperature in 272 Chinese cities from 2013 to 2015.We examined the association between the“total AHI”(based on total mortality)and total mortality,and further compared the ability of the“total AHI”to predict specific cardiopulmonary mortality with that of“specific AHIs”(based on specific mortalities).On average,air pollution and non-optimum temperature were associated with 28.23%of daily excess mortality,of which 23.47%was associated with non-optimum temperature while the remainder was associated with fine particulate matter(PM2.5)(1.12%),NO2(2.29%,),and O3(2.29%).The new AHI uses a 10-point scale and shows an average across all 272 cities of 6 points.The E-R curve for AHI and mortality is approximately linear,without any thresholds.Each one unit increase in“total AHI”is associated with a 0.84%increase in all-cause mortality and 1.01%,0.98%,1.02%,1.66%,and 1.71%increases in cardiovascular disease,coronary heart disease,stroke,respiratory diseases,and chronic obstructive pulmonary disease mortality,respectively.Cause-specific mortality risk estimates using the“total AHI”are similar to those predicted by“specific AHIs.”In conclusion,the“total AHI”proposed herein could be a promising tool for communicating health risks related to exposure to the ambient environment to the public.

The Approach of Compensation of Air Refractive Index in Thermal Expansion Coefficients Measurement Based on Laser Feedback Interferometry

We present the thermal expansion coefficient （TEC） measurement technology of compensating for the effect of variations in the refractive index based on a Nd： YA G laser feedback system, the beam frequency is shifted by a pair of aeousto-optic modulators and then the heterodyne phase measurement technique is used. The sample measured is placed in a muffle furnace with two coaxial holes opened on the opposite furnace walls. The measurement beams hit perpendicularly and coaxially on each surface of the sample. The reference beams hit on the reference mirror and the high-refiectivity mirror, respectively. By the heterodyne configuration and computing, the influences of the vibration, distortion of the sample supporter and the effect of variations in the refractive index are measured and largely minimized. For validation, the TECs of aluminum samples are determined in the temperature range of 29-748K, confirming not only the precision within 5 × 10-7 K-1 and the accuracy within 0.4% from 298K to 448K but also the high sensitivity non-contact measurement of the lower reflectivity surface induced by the sample oxidization from 448 K to 748 K.

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.

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.

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.

Impact of the Winter North Pacific Oscillation on the Surface Air Temperature over Eurasia and North America: Sensitivity to the Index Definition

This study analyzes the impact of the winter North Pacific Oscillation（NPO） on the surface air temperature（SAT）variations over Eurasia and North America based on six different NPO indices. Results show that the influences of the winter NPO on the SAT over Eurasia and North America are sensitive to the definition of the NPO index. The impact of the winter NPO on the SAT variations over Eurasia（North America） is significant（insignificant） when the anticyclonic anomaly associated with the NPO index over the North Pacific midlatitudes shifts westward and pronounced northerly wind anomalies appear around Lake Baikal. By contrast, the impact of the winter NPO on the SAT variations over Eurasia（North America）is insignificant（significant） when the anticyclonic anomaly over the North Pacific related to the NPO index shifts eastward and the associated northerly wind anomalies to its eastern flank extend to North America. The present study suggests that the NPO definition should be taken into account when analyzing the impact of the winter NPO on Eurasian and North American SAT variations.

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.

Relationship between Air Pollution Index (API) and Crowd Health in Nanchang City

Objective: To explore the effect of Air Pollution Index (API) on people’s health. Methods: The data on air pollution index (API), NO2, SO2 and PM10 were based on the everyday monitoring information from environmental monitoring station of Nanchang City. The everyday outpatient service diseases information of 2005 related to air pollution from some First Level Hospitals in Nanchang city was collected, and was summarized and analyzed by statistics software of Excel 2003 and SPSS11.5. Results: The average concentrations of NO2, SO2 and PM10 in the air of Nanchang city from 2006-2009 were 19.70 ± 8.56 μg/m3, 44.60 ± 10.45 μg/m3, 62.30 ± 19.76 μg/m3 respectively. Tight relationship was detected between NO2, SO2 and PM10. Air pollution index (API) can better reflect the air pollution status of Nanchang city. There were positive correlations between API and number of outpatient service diseases, including cardiovascular disease, respiratory disease, ophthalmology disease and ear-nose-throat (ENT) disease in Nanchang city. Conclusion API was related to the number of outpatient service relative diseases.

Statistical Air Pollution Index (API) for Trinidad and Tobago Based on Observed Data on Trinidad’s West Coast

Air pollution has been identified as the largest global environmental threat facing the world today, estimated to cause 7 - 10 million deaths worldwide annually (World Health Organisation, 2014, 2016;Yale University, 2018). Trinidad and Tobago, with a per capita GDP of USD$16310 (2019), is the most industrialised of the Caribbean islands, and like the rest of the Caribbean region is also affected by seasonal Sahara dust (PM2.5). Assessment of the air quality was done for over Trinidad’s west coast. Pollution was measured at four stations during March ‘15-May ‘16, representative of rural, urban, mixed background and industrial land uses. Annual mean PM2.5 and PM10 in ambient air exceeded the WHO guidelines for protection of public health (n = 522). PM2.5 and PM10 exceed the WHO (2006) safe limit guidelines (PM2.5 is 10 μg/m3;PM10 is 20 μg/m3) over 70% of the time sampled at urban and industrial sites. Gaseous pollutants found to be in exceedance were CO, NH3, NO2, N2O, C6H6. Nitrogen dioxide and benzene were the most prolific. A collated metric based on measurement of these pollutants yielded a statistically validated algorithm—An Air Pollution Index. The single metric can convey useful and easily understood information on air quality to the regulators and the general public.

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.

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.

Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia

This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O3, PM10, NO2, CH4, NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.

Use of Ventilation-Index in the Development of Exposure Model for Indoor Air Pollution—A Review

In indoor environment, emission factor of the cooking fuel plays a vital role in determining correlation between exposure assessment and health effects. Both indoor and outdoor air pollution exposures are widely influenced by the ventilation status. An optimum control of the air change rate has also significant impact on the exposure pattern. A number of studies revealed that the indoor particulates and gaseous exposures, resulting from the combustion of various cooking fuels, are associated with significant adverse health effects on pregnant mothers and new born babies. The impacts of ventilation status on air pollution exposure in households’ kitchens or living rooms have not been explored enough. Except a few studies with concrete rooms, especially in industries, no other studies have been established on the correlation between the ventilation index and air pollution exposure. The intent of this review is to discuss reported findings focused on the ventilation and exposure to air pollution. This will obviously help better understanding to modulate exposure profile in household condition using simple tool of ventilation measurement.

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.

城市空气质量指数的时间序列分析和预测--以扬州市为例

城市空气质量指数是反映城市环境宜居水平的重要指标,预测城市空气质量指数可以为政策制定提供重要的参考依据。选取扬州市2014-2022年的空气质量指数(AQI)进行时间序列分析,采用的时间序列模型有自回归积分滑动平均模型(ARIMA)、先知模型(Prophet)以及人工智能领域的长短期记忆模型(LSTM)。通过这些模型分析扬州市空气质量指数的周期性、季节性和趋势特征,并对空气质量指数进行预测。结果显示,基于人工智能的长短期记忆模型具有很强的预测能力。

干旱半干旱地区城市公园绿地空气负离子浓度特征及影响因素

为研究干旱半干旱地区城市公园绿地空气负离子浓度(NAIC)特征及影响因素,以呼和浩特市敕勒川公园为对象,于2020—2021年,每个季节选取晴天、晴间多云为主的天气各10 d,同步监测07:00—19:00时段,5种不同结构绿地(乔草型、乔灌型、乔木型、灌草型、乔灌草型)及对照区(CK)空气负离子浓度及PM10、PM2.5、温度、湿度、风速、大气压强、噪声等环境指标。结果表明:不同结构绿地空气负离子浓度季节日变化有所差异;同一季节,复杂结构绿地空气负离子浓度均值较高,不同结构绿地之间空气负离子浓度均值差异不显著;同一结构绿地,夏、秋季空气负离子浓度均值显著高于春、冬季;不同结构绿地空气清洁度夏、秋季较高,空气清洁度评价指数均值分别为0.58~0.78、0.52~0.80。绿地结构、季节对空气负离子浓度影响为主效应,绿地结构×季节对空气负离子浓度交互作用不显著;除此之外,相对湿度、PM10、PM2.5是影响空气负离子浓度的主要因素。

ADO在慢性阻塞性肺疾病患者近期预后评估中的价值

目的探讨呼吸困难指数气流受限程度指数(dyspnea index air flow restriction degree,ADO)在慢性阻塞性肺疾病(chronic obstructive pulmonary disease,COPD)患者近期预后评估中的价值。方法选取新疆医科大学第二附属医院呼吸内科自2021年3月—2023年3月的COPD患者120例,并依照患者最终转归情况将其分为存活组(n=95)与死亡组(n=25)。观察2组患者的基础病情况及患者性别、年龄、第1秒用力呼气容积(first second forced expiratory volume,FEV1)占预计值的百分比和ADO指数等相关指标。比较ADO指数不同分数患者病死率。比较ADO指数预测180 d死亡的受试者工作特征(receiver operating characteristic,ROC)曲线面积。结果2组患者的高血压、冠心病、心律失常、糖尿病、慢性肝病、慢性肾病、亚临床甲减发生情况对比,差异无统计学意义(P>0.05)。死亡组患者的FEV1占预计值的百分比、FEV1占预计值的百分比评分、呼吸困难分[英国医学研究委员会(the Medical Research Council,MRC)]评分以及ADO指数均高于存活组患者(P<0.05)。ADO指数<5分者的死亡率高于ADO指数≥5分者(P<0.05)。ADO指数预测180 d死亡的ROC曲线面积为0.851(95%CI:0.767~0.928,P<0.001),ADO指数为5.5时,约登指数最大,为0.565。结论ADO可有效反映COPD病情严重程度,对于患者而言可准确反映其病情进展情况,帮助其获得良好的疾病治疗效果,对于患者近期预后而言也具有积极意义,临床应用效果良好。

中国城市PM_(2.5)污染暴露不平等及其驱动因素——基于Theil指数和LMDI分解

聚焦大气污染物PM_(2.5),对2015~2021年污染物浓度进行人口规模赋权,构建中国城市PM_(2.5)污染暴露水平指标,基于GINI系数和Theil指数测度污染暴露不平等,并按城市行政层级、省份进行差异分解;基于对数平均迪氏指数分解法(LMDI),分别对污染暴露水平、不平等程度进行驱动因素分解,探究其变化背后的社会经济因素.结果表明:中国城市PM_(2.5)污染暴露不平等总体温和,GINI系数保持在0.2以内且呈递减趋势.不同行政级别城市间未表现出污染暴露不平等,而省际差异对污染暴露不平等的贡献达68.4%;总体上能源的污染暴露乘数、政府绿色支出能耗强度是PM_(2.5)污染暴露水平的主要抑制因素,对污染暴露水平的驱动贡献超过60%.政府支出结构、支出规模、经济发展和人口因素对PM_(2.5)污染暴露起促增作用,但各因素在不同层级的城市间具有异质性;能源的污染暴露乘数、绿色支出能耗强度对不平等的动态抑制作用弱化,主要依靠能源绿色化的治污策略面临挑战.据此提出更好发挥政府作用,持续降低中国PM_(2.5)污染暴露水平,实现环境公共服务更加均等化的政策启示.

陕西省环境气象条件评估指数时空变化特征研究

基于陕西省10地市2015—2020年PM_(2.5)浓度实况监测数据和气象条件评估指数EMI,统计分析EMI和PM_(2.5)相关性及近年EMI的时空分布特征,定量评估气象条件变化及综合治理措施对陕西环境空气质量的影响。结果表明:陕西省2015—2020年EMI可较好地表征和评估气象条件变化导致的PM_(2.5)浓度变化。2015—2020年EMI呈线性降低趋势,气象条件持续向好。月EMI均呈明显的单谷分布特征,EMI冬半年大,夏半年小。各区域EMI总体上呈关中高、陕南次之、陕北最低的分布特点,关中中部、东部EMI最大。EMI正距平百分比高值区主要分布在关中地区,各年关中均有60%以上面积正距平,其中2016年冬季关中83.0%面积正距平,37.5%面积正距平超过100%。2020年陕西省各区域环境空气质量较2015—2019年均有明显改善,全省PM_(2.5)浓度较前5 a平均降低24.8%,全省气象条件改善使PM2.5浓度较近5 a降低16.4%,排放变化使得PM_(2.5)浓度较前5 a平均下降9.4%,可见,近年陕西省大气污染综合治理措施成果显著,环境质量明显改善。