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.

Spatio-Temporal Air Pollutant Characterization for Urban Areas

Urban pollution has now become increasingly recognized as an important determinant of air pollution in developed countries. The effect of urban air pollution in developing countries, on the other hand, has not been adequately addressed in the data Spatio-temporal time series. Thus, this study was intended to characterize the effect of urbanization on air pollution for an urbanized Klang Valley, Malaysia using Spatio-temporal data from 2008 to 2017. The Air Pollution Index (API) data and local pollutant concentration were employed to establish the links between urban air pollution. The analysis will be supported by determining the source of pollutants during the study period using Principal Component Analysis (PCA). The study identified that Carbon monoxide (CO), Nitrogen Dioxide (NO2), and Ozone (O3) are the major air pollution that has contributed to degrading air quality in the Klang Valley due to the vehicles, combustion process, and industries.

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.

Evaluation of ambient air quality in Guangzhou, China

On the basis of the reported air quality index （API） and air pollutant monitoring data provided by the Guangzhou Environment Monitoring Stations over the last twenty-five years, the characteristics of air quality, prominent pollutants, and variation of the average annual concentrations of SOE, NOE, total suspended particulate （TSP）, fine particulates （PM10）, CO and dustfall in Guangzhou City were analyzed. Results showed that TSP was the prominent pollutant in the ambient air environment of Guangzhou City. Of the prominent pollutants, TSP accounted for nearly 62%, SOE 12.3%, and NOx 6.4%, respectively. The average API of Guangzhou over 6 years was higher than that of Beijing, Tianjin, Nanjing, Hangzhou, Suzhou and Shanghai, and lower than that of Shenzhen, Zhuhai and Shantou. Concentrations of air pollutants have shown a downward trend in recent years, but they are generally worse than ambient air quality standards for USA, Hong Kong and EU. SOE and NOx pollution were still serious, impling that waste gas pollution from all kinds of vehicles had become a significant problem for environmental protection in Guangzhou. The possible causes of worsening air quality were also discussed in this paper.

Characterization of Air Pollution in Urban Areas of Yangtze River Delta,China

The hallmark of development in the Yangtze River Delta(YRD) of East China has been sprawling urbanization. However, air pollution is a significant problem in these urban areas. In this paper, we investigated and analyzed the air pollution index(API) in four cities(Shanghai, Nanjing, Hangzhou and Ningbo) in the YRD from 2001 to 2012. We attempted to empirically examine the relationship between meteorological factors and air quality in the urban areas of the YRD. According to the monitoring data, the API in Shanghai, Nanjing, Hangzhou slightly declined and that in Ningbo increased over the study period. We analyzed the inter-annual, seasonal, and monthly variations of API, from which we found that the air quality had different temporal changes in the four cities. It was indicated that air quality was poor in winter and spring and best in summer. Furthermore, different weather conditions affected air quality level. The wind direction was considered as an important and influential factor to air pollution, which has an impact on the accumulating or cleaning processes of pollutants. The air quality was influenced by the different wind directions that varied with seasons and cities.

TYPICAL WEATHER CHARACTERISTICS ASSOCIATED WITH AIR POLLUTION IN HONG KONG AREA

With the hourly data of Air Pollution Index （AP1） by Hong Kong Environmental Protection Department （HKEPD） during the 6 years of 2000 - 2005 and NCEP / NCAR reanalysis data of 2.5°× 2.5° wind and pressure fields, the characteristics of API in Hong Kong area and the impacts of typical weather characteristics on the air pollution in Hong Kong have been studied. The results are shown as follows. （1） The API exhibits obvious seasonal variability as the number of air pollution days increases by the year. For most of the local monitoring stations, it is the most from January to March, a little less from July to September and the least from April to June. （2） There are four typical types of weather situations that are responsible for the air pollution in Hong Kong： tropical cyclones, continental cold highs, transformed highs that have moved out to sea and low pressure troughs.

Air Pollution Interpolation Analysis Based on Measured and Simulated Sites in Nanjing

Based on the statistical analysis of API （air pollution index）, the study improves the layout of the site in the downtown of Nanjing and the surroundings. Through selecting more relevant factors to establish the API regression equation and making the inversion of API data in simulated sites, the interpolation values of API in both actual sites and simulated sites have been calculated. The methods include IDW （inverse distance weighting） interpolation, Spline interpolation, and Kriging interpolation Spherical model, Exponential model and the Gaussian model. Meanwhile, through the cross-validation to test the results of interpolation in different models or parameters, the study also obtains the best fit of the interpolation model or parameters. In addition, IDW p = 3, fitting coefficient of 0.644; Spline interpolation w = 1, the fitting coefficient of 0.972; Kriging interpolation, Gaussian, fitting coefficient of 0.684. The study indicates that in best fitting model, the parameters after in increasing the simulated site are not in line with the ones previous. The result shows that it is best to test different data separately and select the appropriate interpolation model, but not blindly use the same spatial interpolation. After the increasing of the stimulated site, the API estimated results in three interpolation methods are consistent with the spatial distribution trend. In the aspect of calculating the range, the improvement close the results between 3 interpolation methods and increase of the stimulated sites, and the values of Spline interpolation and Kriging interpolation is closer.

Association between medical resources and the proportion of oldest-old in the Chinese population

The potential association between medical resources and the proportion of oldest-old(90 years of age and above)in the Chinese population was examined,and we found that the higher proportion of oldest-old was associated with the higher number of beds in hospitals and health centers.

API-based assessment on urban air environment bearing capability in China

Given the complexity and time-consuming of the conventional environmental capacity based assessment on air environment carrying capacity; a new method for assessing urban air environment carrying capacity based on air pollution index （API） is presented. By using this new method, the air environmental beating capability of 333 cities at the prefecture level and above is assessed. The results show that of the 333 cities 9.6% is of high beating capability, 34.5% relatively high beating capability, 52.6% medium beating capability, 2.7% low capability, and 0.6% is of weak beating capability; in terms of regional distribution, the western region is of relatively high air environment bearing capability, followed by north-eastern and eastern regions, and the ambient air quality in the middle region is quite poor; among the 12 urban agglomerations in key regions, Pearl River delta, west side of Taiwan Strait and Chengdu-Chongqing agglomera- tions are of relatively high carrying capacity while other agglomerations are of medium beating capability. The assessment results imply that the existing air quality standard （GB3095-1996） is quite unsound.

RELATIONSHIP BETWEEN HORIZONTAL EXTINCTION COEFFICIENT AND PM_(10) CONCENTRATION IN XI'AN,CHINA,DURING 1980-2002

Based on daily visibility data obtained from 1980-2002 and air pollution index data from 2001-2004 in Xi＇an, long-term variations and relationships for daily horizontal extinction coefficient and mass concentration of PM10 have been evaluated. A decreasing trend was found in horizontal extinction coefficient during the past 23 years, with higher values observed in 1980s relative to 1990s, and the highest and lowest values in winter and summer, respectively. Significant correlation and similar seasonal variations existed between horizontal extinction coefficient and PM10 concentration, suggesting the high influence of PM10 to the visibility drop at a site in the Guanzhong Plain of central China during the past two decades.

Online monitoring of water-soluble ionic composition of PM_(10)during early summer over Lanzhou City

Lanzhou is one of the most aerosol-polluted cities in China. In this study, an online analyzer for Monitoring for AeRosols and GAses was deployed to measure major water-soluble inorganic ions in PM10 at 1-hour time resolution, and 923 samples were obtained from Apr 1 to May 24, 2011. During the field campaign, air pollution days were encountered with Air Quality Index more than 100 and daily average concentration of PM10 exceeding 150 itg/m3. Based on the variation of water-soluble ions and results of Positive Matrix Factorization 3.0 model execution, the air pollution days were classified as crustal species- or secondary aerosol-induced, and the different formation mechanisms of these two air pollution types were studied. During the crustal species pollution days, the content of Ca2＋ increased and was about 2.3 times higher than the average on clear days, and the air parcel back trajectory was used to analyze the sources of crustal species. Data on sulfate, trace gases and meteorological factors were used to reveal the formation mechanism of secondary aerosol pollution. The sulfur oxidation ratio （SOR） was derived from the 923 samples, and the SOR had high positive correlation with relative humidity in early summer in Lanzhou.