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.

Impact of air pollution on urbanization:evidence at China’s city level

This paper investigates the effect and transmission mechanism of air pollution on urbanization based on data from China’s 107 cities during 2005–2018.In order to identify the impact of air pollution on China’s urbanization,we utilized night light data to represent the level of urbanization and used temperature inversion as an instrumental variable to mitigate endogeneity within the two-stage least squares framework.The results suggest that air pollution significantly slowed China’s urbanization process with economic growth acting as the transmission mechanism.The heterogeneity analyses revealed that air pollution had a greater negative impact on urbanization in northern regions than that in southern regions,and a greater negative impact in resource-oriented cities than that in non-resource-based cities.We also find that air pollution was to the detriment of urbanization in larger cities,which have more than 3 million residents,while it did not have a significant impact on Type II large cities,which have fewer than 3 million residents.

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%.

Climate change,ambient air pollution,and students'mental health

The impact of global climate change and air pollution on mental health has become a crucial public health issue.Increased public awareness of health,advancements in medical diagnosis and treatment,the way media outlets report environmental changes and the variation in social resources affect psychological responses and adaptation methods to climate change and air pollution.In the context of climate change,extreme weather events seriously disrupt people's living environments,and unstable educational environments lead to an increase in mental health issues for students.Air pollution affects students'mental health by increasing the incidence of diseases while decreasing contact with nature,leading to problems such as anxiety,depression,and decreased cognitive function.We call for joint efforts to reduce pollutant emissions at the source,improve energy structures,strengthen environmental monitoring and governance,increase attention to the mental health issues of students,and help student groups build resilience;by establishing public policies,enhancing social support and adjusting lifestyles and habits,we can help students cope with the constantly changing environment and maintain a good level of mental health.Through these comprehensive measures,we can more effectively address the challenges of global climate change and air pollution and promote the achievement of the United Nations Sustainable Development Goals.

Recent Progress in Atmospheric Chemistry Research in China: Establishing a Theoretical Framework for the “Air Pollution Complex”

Atmospheric chemistry research has been growing rapidly in China in the last 25 years since the concept of the“air pollution complex”was first proposed by Professor Xiaoyan TANG in 1997.For papers published in 2021 on air pollution(only papers included in the Web of Science Core Collection database were considered),more than 24000 papers were authored or co-authored by scientists working in China.In this paper,we review a limited number of representative and significant studies on atmospheric chemistry in China in the last few years,including studies on(1)sources and emission inventories,(2)atmospheric chemical processes,(3)interactions of air pollution with meteorology,weather and climate,(4)interactions between the biosphere and atmosphere,and(5)data assimilation.The intention was not to provide a complete review of all progress made in the last few years,but rather to serve as a starting point for learning more about atmospheric chemistry research in China.The advances reviewed in this paper have enabled a theoretical framework for the air pollution complex to be established,provided robust scientific support to highly successful air pollution control policies in China,and created great opportunities in education,training,and career development for many graduate students and young scientists.This paper further highlights that developing and low-income countries that are heavily affected by air pollution can benefit from these research advances,whilst at the same time acknowledging that many challenges and opportunities still remain in atmospheric chemistry research in China,to hopefully be addressed over the next few decades.

Type 2 diabetes and thyroid cancer:Synergized risk with rising air pollution

Diabetes is a complex condition,and the causes are still not fully understood.However,a growing body of evidence suggests that exposure to air pollution could be linked to an increased risk of diabetes.Specifically,exposure to certain pollutants,such as particulate Matter and Ozone,has been associated with higher rates of diabetes.At the same time,air pollution has also been linked to an increased risk of thyroid cancer.While there is less evidence linking air pollution to thyroid cancer than to diabetes,it is clear that air pollution could have severe implications for thyroid health.Air pollution could increase the risk of diabetes and thyroid cancer through several mechanisms.For example,air pollution could increase inflammation in the body,which is linked to an increased risk of diabetes and thyroid cancer.Air pollution could also increase oxidative stress,which is linked to an increased risk of diabetes and thyroid cancer.Additionally,air pollution could increase the risk of diabetes and thyroid cancer by affecting the endocrine system.This review explores the link between diabetes and air pollution on thyroid cancer.We will discuss the evidence for an association between air pollution exposure and diabetes and thyroid cancer,as well as the potential implications of air pollution for thyroid health.Given the connections between diabetes,air pollution,and thyroid cancer,it is essential to take preventive measures to reduce the risk of developing the condition.

Meteorological factors, ambient air pollution, and daily hospital admissions for depressive disorder in Harbin: A time-series study

BACKGROUND The literature has discussed the relationship between environmental factors and depressive disorders;however,the results are inconsistent in different studies and regions,as are the interaction effects between environmental factors.We hypo-thesized that meteorological factors and ambient air pollution individually affect and interact to affect depressive disorder morbidity.AIM To investigate the effects of meteorological factors and air pollution on depressive disorders,including their lagged effects and interactions.METHODS The samples were obtained from a class 3 hospital in Harbin,China.Daily hos-pital admission data for depressive disorders from January 1,2015 to December 31,2022 were obtained.Meteorological and air pollution data were also collected during the same period.Generalized additive models with quasi-Poisson regre-ssion were used for time-series modeling to measure the non-linear and delayed effects of environmental factors.We further incorporated each pair of environ-mental factors into a bivariate response surface model to examine the interaction effects on hospital admissions for depressive disorders.RESULTS Data for 2922 d were included in the study,with no missing values.The total number of depressive admissions was 83905.Medium to high correlations existed between environmental factors.Air temperature(AT)and wind speed(WS)significantly affected the number of admissions for depression.An extremely low temperature(-29.0℃)at lag 0 caused a 53%[relative risk(RR)=1.53,95%confidence interval(CI):1.23-1.89]increase in daily hospital admissions relative to the median temperature.Extremely low WSs(0.4 m/s)at lag 7 increased the number of admissions by 58%(RR=1.58,95%CI:1.07-2.31).In contrast,atmospheric pressure and relative humidity had smaller effects.Among the six air pollutants considered in the time-series model,nitrogen dioxide(NO_(2))was the only pollutant that showed significant effects over non-cumulative,cumulative,immediate,and lagged conditions.The cumulative effect of NO_(2) at lag 7 was 0.47%(RR=1.0047,95%CI:1.0024-1.0071).Interaction effects were found between AT and the five air pollutants,atmospheric temperature and the four air pollutants,WS and sulfur dioxide.CONCLUSION Meteorological factors and the air pollutant NO_(2) affect daily hospital admissions for depressive disorders,and interactions exist between meteorological factors and ambient air pollution.

Green Walls as Mitigation of Urban Air Pollution:A Review of Their Effectiveness

Mitigation of urban air pollution has been constrained by the availability of urban spaces for greening.Green walls offer the prospect of greening spaces and surfaces without requiring large areas.Green walls can largely be divided into green facades where the aboveground parts of plants rooted in soil and pots grow directly on,and living walls holding bags,planter tiles,trays and vessels containing substrates in which plants are grown.Green facades and living walls can be continuous or modular with repeating units that can be assembled for extension.This review aims to pres­ent the effectiveness of green walls in removing different types of air pollutants in indoor and outdoor environments.It examined more than 45 peer-reviewed recently published scholarly articles to achieve the aim.It highlights that most of the studies on green walls focus on particulate matter removal and green walls could effectively remove particu­late matter though the effectiveness varies with plant types,air humidity,rainfall and its intensity,leaf area index and contact angle,green wall surface coverage ratio,as well as the height of green walls.Increasing the height of green walls and optimizing their distance from roadsides could promote the deposition of particulate matter.Washing off could regenerate plant surfaces for capturing pollutants.Green walls are also effective in removing NO2,O3,SO2 and CO.Indoor active living walls,when properly designed,could have air purifying performance comparable to a HVAC system.The performance of green walls could be optimized through polycultures,selection of plants,surface coverage and height,and air inflow.

Air Pollution, Global Warming and Difficulties to Replace Fossil Fuel with Renewable Energy

Since the Industrial Revolution, greenhouse gas (GHG) emissions have greatly increased with the increased use of fossil fuels, leading to air pollution and global warming. We present the researches on air pollution and the use of fossil fuels in north China, the economic zone of Changsha-Zhuzhou-Xiangtan and the economic zone of the Pearl River Delta region. Researches indicate that the use of fossil fuels has been the main source of air pollution in the three regions. We present researches on global mean surface temperature (GMST) with the rise of carbon dioxide concentration (CDC) and global fossil fuel consumption (GFFC);researches indicate that the rise in CDC can account for 91% of the rise in GMST, and GFFC can account for 90% of the rise in GMST. We analyse the factors that bring about air pollution and temperature rise, they are the use of fossil fuels and deforestation. It is critically important to replace fossil fuels with clean energy, but renewable energy has also disadvantages. The world faces difficulties in solving air pollution and global warming, so governments of the world should cooperate to solve the technologies of clean energy, and preserve the forests and the natural environment.

Evolution of Meteorological Conditions during a Heavy Air Pollution Event under the Influence of Shallow Foehn in Urumqi, China

The air pollution in Urumqi which is located on the northern slope of the Tianshan Mountains in northwestern China,is very serious in winter.Of particular importance is the influence of terrain-induced shallow foehn,known locally as elevated southeasterly gale(ESEG).It usually modulates atmospheric boundary layer structure and wind field patterns and produces favorable meteorological conditions conducive to hazardous air pollution.During 2013-17,Urumqi had an average of 50 d yr-1 of heavy pollution(daily average PM2.5 concentration>150μg m-3),of which 41 days were in winter.The majority(71.4%)of heavy pollution processes were associated with the shallow foehn.Based on microwave radiometer,wind profiler,and surface observations,the surface meteorological fields and boundary layer evolution during the worst pollution episode in Urumqi during 16-23 February 2013 are investigated.The results illustrate the significant role of shallow foehn in the building,strengthening,and collapsing of temperature inversions.There were four wind field patterns corresponding to four different phases during the whole pollution event.The most serious pollution phase featured shallow foehn activity in the south of Urumqi city and the appearance of an intense inversion layer below 600 m.Intense convergence caused by foehn and mountain-valley winds was sustained during most of the phase,resulting in pollutants sinking downward to the lower boundary layer and accumulating around urban area.The key indicators of such events identified in this study are highly correlated to particulate matter concentrations and could be used to predict heavy pollution episodes in the feature.

Low ambient temperature and air pollution are associated with hospitalization incidence of coronary artery disease:Insights from a cross-sectional study in Northeast China

Background:Previous studies have established a link between fluctuations in climate and increased mortality due to coronary artery disease(CAD).However,there remains a need to explore and clarify the evidence for associations between meteorological changes and hospitalization incidences related to CAD and its subtypes,especially in cold regions.This study aimed to systematically investigate the relationship between exposure to meteorological changes,air pollutants,and hospitalization for CAD in cold regions.Methods:We conducted a cross-sectional study using hospitalization records of 86,483 CAD patients between January 1,2009,and December 31,2019.Poisson regression analysis,based on generalized additive models,was applied to estimating the influence of hospitalization for CAD.Results:Significant associations were found between low ambient temperature[-10℃,RR=1.65;95%CI:(1.28-2.13)]and the incidence of hospitalization for CAD within a lag of 0-14 days.Furthermore,O_(3)[95.50μg/m^(3),RR=12;95%CI:(1.03-1.21)]and NO_(2)[48.70μg/m^(3),RR=1.0895%CI:(1.01-1.15)]levels were identified as primary air pollutants affecting the incidence of CAD,ST-segment-elevation myocardial infarction(STEMI),and non-STEMI(NSTEMI)within the same lag period.Furthermore,O_(3)[95.50μg/m^(3),RR=1.12;95%CI:(1.03-1.21)]and NO_(2)[48.70μg/m^(3),RR=1.0895%CI:(1.01-1.15)]levels were identified as primary air pollutants affecting the incidence of CAD,ST-segment-elevation myocardial infarction(STEMI),and non-STEMI(NSTEMI)within the same lag period.The effect curve of CAD hospitalization incidence significantly increased at lag days 2 and 4 when NO_(2)and O_(3)concentrations were higher,with a pronounced effect at 7 days,dissipating by lag 14 days.No significant associations were observed between exposure to PM,SO_(2),air pressure,humidity,or wind speed and hospitalization incidences due to CAD and its subtypes.Conclusion:Our findings suggest a positive correlation between short-term exposure to low ambient temperatures or air pollutants(O_(3)and NO_(2))and hospitalizations for CAD,STEMI,and NSTEMI.These results could aid the development of effective preparedness strategies for frequent extreme weather events and support clinical and public health practices aimed at reducing the disease burden associated with current and future abnormal weather events.

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.

How Attitudes towards Air Pollution May Impact Public Health: A Case Study of Almaty, Kazakhstan

Using an original public opinion survey, we study public attitudes and behaviors toward air pollution in Almaty, Kazakhstan. In the Health Belief Model (HBM) framework previously used to understand an individual’s health decision-making, we evaluate citizens’ awareness of the poor air quality, their perception of risk, and their willingness to devote time and resources to reduce their air pollution exposure. We find that although citizens are aware of the gravity and general harms of air pollution, they significantly underestimate their individual health risks, and, as a result, often engage in daily routines that exacerbate their exposure to pollution. We find that behaviors increasing the risk of pollution exposure are related to the underlying beliefs about personal health risks, self-efficacy, and material and economic limitations. This means that treating pollution as an individual health problem rather than social issue in public discourse may promote behaviors reducing exposure and improving personal and public health outcomes.

The Effect of Foreign Direct Investment on Air Pollution in the Economic Community of West African States region: What Influence Does Tax Expenditure Have?

Air pollution is one of the crucial environmental challenges facing the countries of the Economic Community of West African States (ECOWAS). The objective of this paper is to examine the effect of an attractive tax policy on the relationship between Foreign Direct Investment (FDI) and air pollution in ECOWAS region over the period 2000 to 2019. By using the Ordinary Least Squares (OLS) method and panel data analyses (fixed effects and random effects), the results show that, in general, FDI does not have a significant effect on air pollution in the region. However, closer analysis reveals that an interaction between FDI and an attractive tax policy has a negative effect on air quality, leading to an increase in air pollution. Thus, companies attracted by tax incentives may not meet rigorous environmental standards. These results highlight the importance for policymakers to balance economic incentives with environmental protection in ECOWAS. Attractive tax policies can stimulate investment, but they must be designed in a way that encourages environmentally friendly practices, thereby helping to improve air quality in the region.

Air Pollution Risk Assessment Using GIS and Remotely Sensed Data in Kirkuk City,Iraq

According to World Health Organization(WHO)estimates and based on a world population review,Iraq ranks tenth among the most air-polluted countries in the world.In this study,the authors tried to evaluate the outdoor air of Kirkuk City north of Iraq.The authors relied on two types of data:field measurements and remotely sensed data.Fifteen air quality points were determined in the study region representing the monthly average measurements im­plemented for the one-year dataset.Geographic information systems(GIS)based geo-statistic and geo-processing techniques have been applied to collected data.Spatial distribution data related to Air Quality Index(AQI),and Par­ticulate Matter(PM10 and PM2.5)were obtained by mapping collected records.Remotely sensed data of PM2.5 were analyzed and compared with the collected data.Health impacts were assessed per each air pollutant determined in the study.Spatial distribution maps revealed the hazardous air type in the study area.Overall AQI ranged between 300 and 472μg/m^(3)referring to unhealthy,very unhealthy,and hazardous classes of pollution.Also,PM10 ranged between 300 and 570μg/m^(3)indicating the same class of air pollution from unhealthy to hazardous.While PM2.5 ranged between 40 and 60μg/m^(3)which represents unhealthy air for sensitive persons and unhealthy air.The remotely sensed data re­vealed different air types for the study period ranging from 14.5 to 52.5μg/m^(3)represented in moderate and unhealthy air for sensitive persons.Significant correlations were obtained where the mean local R2(coefficient of determination)was obtained as 0.83.The assessed data were within high air pollution that requires immediate intervention for con­trolling causes and eliminating their effects.

Impact of Air Pollutants on Lung Function and Inflammatory Response in Asthma in Shanghai

Objective Air pollution is a leading public health issue.This study investigated the effect of air quality and pollutants on pulmonary function and inflammation in patients with asthma in Shanghai.Methods The study monitored 27 asthma outpatients for a year,collecting data on weather,patient self-management[daily asthma diary,peak expiratory flow(PEF)monitoring,medication usage],spirometry and serum markers.To explore the potential mechanisms of any effects,asthmatic mice induced by ovalbumin(OVA)were exposed to PM_(2.5).Results Statistical and correlational analyses revealed that air pollutants have both acute and chronic effects on asthma.Acute exposure showed a correlation between PEF and levels of ozone(O_(3))and nitrogen dioxide(NO_(2)).Chronic exposure indicated that interleukin-5(IL-5)and interleukin-13(IL-13)levels correlated with PM_(2.5)and PM_(10)concentrations.In asthmatic mouse models,exposure to PM_(2.5)increased cytokine levels and worsened lung function.Additionally,PM_(2.5)exposure inhibited cell proliferation by blocking the NF-κB and ERK phosphorylation pathways.Conclusion Ambient air pollutants exacerbate asthma by worsening lung function and enhancing Th2-mediated inflammation.Specifically,PM_(2.5)significantly contributes to these adverse effects.Further research is needed to elucidate the mechanisms by which PM_(2.5)impacts asthma.

Air Pollution Prediction in Warri and Its Environs Using Quality Parameters

Air pollution is a major environmental problem in the Niger Delta Area (NDA) of Nigeria primarily due to oil and gas-related operational activities. Identifiable sources of air pollution in the NDA include gas flaring, vehicle emissions from internal combustion engines, crude oil pollution, etc. The aim of this research is to evaluate the concentration of air pollutants from crude oil-related activities using air quality parameters in Warri during seasons peculiar to the area of study. The Warri metropolis, one of Nigeria’s largest oil cities, was the sampling region under research in this study. An Aeroqual handheld mobile multi-gas monitor fitted with different sensors of (Carbon Monoxide (CO), air quality multi-meter for Particulate Matter (PM2.5 and PM10), Volatile Organic (VOC), Sulphur dioxide (SO2), Ammonia (NH3), Methane (CH4), and air quality index (AQI), was used for the collection of air quality parameter. Linear regression was used to create the model, which was then used to predict the extent of pollution in the locations of study. The average mean concentrations of air pollutants such as CO, NO2, CH4, VOC, NH4, and SO2 were measured at all sampling sites during wet and dry seasons. The results showed that the levels of these pollutants were above the WHO permissible limits for the majority of the air quality parameters studied in all sixteen locations. The concentration levels of most of the pollutants were higher in the dry season than in the rainy season. The study also found that the pollutants were mainly from fossil fuel combustion and road traffic emissions. Overall, the research provided monitoring data for all air quality pollutants under investigation in the study area and demonstrated that these concentrations exceed regulatory guidelines.

Indoor Air Pollution and Its Determinants in Household Settings in Jaipur, India

Individuals spend 90% of their time indoors, primarily at home or at work. Indoor environmental factors have a significant impact on human well-being. It was a longitudinal study that assessed the major factors that reduce indoor air quality, namely particulate matter, and bio-aerosols, using low-cost sensors and the settle plate method, respectively also to determine the effect of atmospheric parameters and land use patterns in households of commercial, industrial, residential, slum, and rural areas of the city. PM2.5 concentration levels were similar in most parts of the day across all sites. PM10.0 concentration levels increased indoors in a commercial area. PM2.5 concentration showed a negative correlation with temperature and a positive correlation with relative humidity in some areas. Very high values of PM2.5 concentration and PM10.0 concentration have been observed in this study, inside households of selected rural and urban areas. Pathogenic gram-positive cocci, gram-positive rods, Aspergillus, and Mucor species were the most common bacterial and fungal species respectively found inside households. This study examined particulate matter concentration along with bio-aerosols, as very less studies have been conducted in Jaipur the capital of Rajasthan, a state in the western part of India which assessed both of these factors together to determine the indoor air quality. Rural households surrounding the periphery of the city were found to have similar pollution levels as urban households. So, this study may form the basis for reducing pollution inside households and also for taking suitable measures for the reduction of pollution in the indoor environment.

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.

Enhancing Patients Outcomes and Infection Control through Smart Indoor Air Quality Monitoring Systems

Air pollution poses a critical threat to public health and environmental sustainability globally, and Nigeria is no exception. Despite significant economic growth and urban development, Nigeria faces substantial air quality challenges, particularly in urban centers. While outdoor air pollution has received considerable attention, the issue of indoor air quality remains underexplored yet equally critical. This study aims to develop a reliable, cost-effective, and user-friendly solution for continuous monitoring and reporting of indoor air quality, accessible from anywhere via a web interface. Addressing the urgent need for effective indoor air quality monitoring in urban hospitals, the research focuses on designing and implementing a smart indoor air quality monitoring system using Arduino technology. Employing an Arduino Uno, ESP8266 Wi-Fi module, and MQ135 gas sensor, the system collects real-time air quality data, transmits it to the ThingSpeak cloud platform, and visualizes it through a user-friendly web interface. This project offers a cost-effective, portable, and reliable solution for monitoring indoor air quality, aiming to mitigate health risks and promote a healthier living environment.