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.

Air Quality Assessment of Ubeji Community near Petroleum-Related Activities

The escalating global concern over air pollution requires rigorous investigations. This study assesses air quality near residential areas affected by petroleum-related activities in Ubeji Community, utilizing Aeroqual handheld mobile multi-gas monitors and air quality multi-meters. Air sampling occurred on three distinct days using multi-gas monitors and meters, covering parameters such as CO, NO2, CH4, NH3, VOCs, Particulate Matter, Temperature, Relative Humidity, and Air Quality Index. Soil and plant samples were collected and analyzed for physicochemical and organic components. Air pollutant concentrations showed significant fluctuations. Carbon monoxide (CO) ranged from 0.00 to 3.22 ppm, NO2 from 0.00 to 0.10 ppm, CH4 from 4.00 to 2083 ppm, NH3 from 371 to 5086 ppm, and VOCs from 414 to 6135 ppm. Soil analysis revealed low total nitrogen, and undetected BTEX levels. Plant samples displayed a pH range of 7.72 to 9.45. CO concentrations, although below WHO limits, indicated potential vehicular and industrial influences. Fluctuations in NO2 and CH4 were linked to traffic, industrial activities, and gas flaring. NH3 levels suggested diverse pollution sources. The result in this study highlights the dynamic nature of air pollution in Ubeji community, emphasizing the urgent need for effective pollution control measures. Although CO concentrations were within limits, continuous monitoring is essential. Elevated NO2 levels gave information on the impact of industrial activities, while high CH4 concentrations may be associated with gas flaring and illegal refining. The study recommends comprehensive measures and collaborative efforts to address these complex issues, safeguarding both the environment and public health. This study shows the potential synergy between air quality sensors and plants for holistic environmental health assessments, offering valuable insights for environmental assessments and remediation endeavours. The findings call for stringent regulations and collaborative efforts to address air pollution in Ubeji community comprehensively.

Distribution of Atmospheric Pollution in Southern Benin

This study focuses on air quality in southern Benin in order to show public authorities what the Beninese populations are exposed to for efficient decision-making. Two sampling campaigns were carried out, one in the wet period and the other in the dry season. The measurements were taken using a monitor called an “Air Quality Monitor”. For data processing, the multiple comparison methods of Dun (1961) and the Wilcoxon test were used. To maintain legitimacy, all spatial data were included in the official cartographic repository of Benin: WGS 1984, Transverse Mercator Universe Projection (UTM), Zone 31 North. The Moran statistic was used to measure the levels of spatial autocorrelation of the variables studied and to test the significance. In order to locate the spatial subsets, the local spatial association indices of Anselin Local Moran and Getis-Ord, Gi* were used. In terms of results, on the 13 monitoring sites and the 8 parameters chosen to determine air quality, we do not note any significant inter-seasonal difference. Of the eight parameters, only three parameters present spatial autocorrelation leading to predictions of ambient air quality over the entire study area based on the distance separating the points, namely, PM2.5, PM10 and ambient air quality index (AQI). The localities affected by atmospheric pollution in South Benin are located in the south-western part of Benin, headed by Cotonou, which is heavily polluted by CO2, TCOV, PM10 and PM2.5.

June to October Aerosol Optical Depth over the Arctic at Various Spatial and Temporal Scales in MODIS, MAIAC, CALIOP and GOES Data

The accuracy of the cloud-aerosol lidar with orthogonal polarization (CALIOP), moderate resolution imaging spectroradiometer (MODIS), Multi-Angle Implementation of Atmospheric Correction (MAIAC), and Geostationary Operational Environmental Satellite (GOES) aerosol optical depth (AOD) products for the Arctic north of 59.75°N was examined by means of 35 aerosol robotic network (AERONET) AOD sites. The assessment for June to October 2006 to 2020 showed MAIAC AOD agreed the best with AERONET AOD;CALIOP AOD differed the strongest from the AERONET AOD. Cross-correlations of CALIOP AOD along the satellite path indicated that AOD-values 40 km up-and-down the path often failed to represent the AERONET AOD-values within ±30 min of the overpass in this region dominated by easterly winds. Typically, CALIOP AOD was lower than AERONET AOD and MAIAC AOD at the sites, especially, at sites with mean AOD below 0.1. Generally, MODIS AOD values exceeded those of MAIAC. Comparison of CALIOP, MAIAC, and MODIS products resampled on a 0.25° × 0.25° grid revealed differences among the products caused by their temporal and spatial resolution, sample habit and size. Typically, the MODIS AOD-product showed the most details in AOD distribution. Despite differences in AOD-values, all products provided similar temporal evolution of elevated and lower AOD.

Comparative Analysis of PM10 Emission Rates from Controlled and Uncontrolled Cement Silos in Concrete Batching Facilities

This research study quantifies the PM10 emission rates (g/s) from cement silos in 25 concrete batching facilities for both controlled and uncontrolled scenarios by applying the USEPA AP-42 guidelines step-by-step approach. The study focuses on evaluating the potential environmental impact of cement dust fugitive emissions from 176 cement silos located in 25 concrete batching facilities in the M35 Mussafah industrial area of Abu Dhabi, UAE. Emission factors are crucial for quantifying the PM10 emission rates (g/s) that support developing source-specific emission estimates for areawide inventories to identify major sources of pollution that provide screening sources for compliance monitoring and air dispersion modeling. This requires data to be collected involves information on production, raw material usage, energy consumption, and process-related details, this was obtained using various methods, including field visits, surveys, and interviews with facility representatives to calculate emission rates accurately. Statistical analysis was conducted on cement consumption and emission rates for controlled and uncontrolled sources of the targeted facilities. The data shows that the average cement consumption among the facilities is approximately 88,160 (MT/yr), with a wide range of variation depending on the facility size and production rate. The emission rates from controlled sources have an average of 4.752E-04 (g/s), while the rates from uncontrolled sources average 0.6716 (g/s). The analysis shows a significant statistical relationship (p < 0.05) and perfect positive correlation (r = 1) between cement consumption and emission rates, indicating that as cement consumption increases, emission rates tend to increase as well. Furthermore, comparing the emission rates from controlled and uncontrolled scenarios. The data showed a significant difference between the two scenarios, highlighting the effectiveness of control measures in reducing PM10 emissions. The study’s findings provide insights into the impact of cement silo emissions on air quality and the importance of implementing control measures in concrete batching facilities. The comparative analysis contributes to understanding emission sources and supports the development of pollution control strategies in the Ready-Mix industry.

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.

Outlier Detection of Air Quality for Two Indian Urban Cities Using Functional Data Analysis

Human living would be impossible without air quality. Consistent advancements in practically every aspect of contemporary human life have harmed air quality. Everyday industrial, transportation, and home activities turn up dangerous contaminants in our surroundings. This study investigated two years’ worth of air quality and outlier detection data from two Indian cities. Studies on air pollution have used numerous types of methodologies, with various gases being seen as a vector whose components include gas concentration values for each observation per-formed. We use curves to represent the monthly average of daily gas emissions in our technique. The approach, which is based on functional depth, was used to find outliers in the city of Delhi and Kolkata’s gas emissions, and the outcomes were compared to those from the traditional method. In the evaluation and comparison of these models’ performances, the functional approach model studied well.

Impact of Improved Cookstoves on the Level of Household Exposure to CO and PM2.5 in Sub-Saharan Cities: The Case of the City of Ouagadougou

Air pollution is one of the major global threats to human health. In Burkina Faso, more than 80% of the population uses solid fuels as the main source of cooking energy. This paper reports a comparative study on the exposure of household to the carbon monoxide (CO) and particulate matter (PM2.5) emitted by improved cookstoves (ICS) or traditional cookstoves (TCS). A cross-sectional study was conducted in the city of Ouagadougou for 4 months during the rainy season (July to October) in households with an outdoor kitchen. The investigation involved 92 households where air pollutants, such as PM2.5 and CO were measured with Indoor Air Pollution Meters (IAP meter). These measurements were focused on the concentration levels of the pollutants during cooking. The results of this study show high levels of PM2.5 and CO for all type of stoves. Wood stoves led to higher PM2.5 and lower CO emissions than charcoal stoves. ICS reduce emissions of indoor air pollutants compared to TCS. This reduction raised up to 82% for PM2.5 and 37% for CO. The analysis of the data measured with the student test (t-test) shows that there is a statistically significant difference between the average values of the concentrations of the pollutants emitted with the TCS compared to ICS, except for CO emissions measured on multi-pot sizes cookstoves (MM). This study shows that the concentrations of indoor air pollutants are very high regardless of the type of cookstoves used. The CO exposure obtained varies from 119.10 to 362.72 μg/m3 for 15-minute and 10.83 - 55.11 μg/m3 for 1-hour exposure. The exposure in PM2.5 varies from 4762 to 16,257 μg/m3 for 15-minute and 106.63 to 1597 μg/m3 for 1-hour of exposure. It was noted that the CO exposure levels obtained over 15-minute of exposure are 1.36 to 4.15 times higher than the WHO recommendation and 1.8 times higher for an exposure time of one hour. This means that women in charge of cooking have a high risk of exposure to air pollutants.

Unveiling Citizens’ Perceptions, Attitudes, and Behaviors towards Air Quality: Insights from a Survey in the Po Basin Area

Air pollution has far-reaching environmental and social consequences, requiring the active participation of individual citizens in improving air quality by means of emission-reducing behaviors. This research examines the relationship between citizens’ knowledge, perceptions of air quality, attitudes towards policy measures, and intentions to adopt environmentally-friendly behaviors to combat air pollution. A comprehensive survey is conducted among a representative sample from seven regions in the Po basin area: Emilia-Romagna, Friuli-Venezia Giulia, Lombardy, Piedmont, Province of Trento, Valle d’Aosta, and Veneto. The survey aims at profiling participants based on their level of information, perceptions of air pollution, and attitudes towards emission-reducing behaviors. Cluster analysis identifies meaningful differences among citizen groups in terms of their awareness and intentions to engage in specific behaviors. Four distinct clusters emerge, each characterized by varying levels of willingness to embrace pro-environmental behaviors and support air quality improvement initiatives. By examining these profiles, the study uncovers patterns in citizens’ awareness, concerns, and acceptance of environmentally-friendly practices. The findings offer valuable insights for policymakers to develop targeted interventions, policies, and communication strategies.

Role of Meteorological Parameters in COVID-19 Pandemic Waves in Delhi, India

In this study, the levels of meteorological parameters like maximum temperature (°F), relative temperature (°F), minimum temperature (°F), humidity (%), dew point (°F), wind speed (mph), rainfall (in), and air pressure (in) were analyzed for all three COVID-19 pandemic waves in the NCT of Delhi, India. After doing statistical analysis, the results showed that only a few parameters, like temperature (maximum, minimum, and relative), dew point, humidity, and air pressure, were linked to the start of COVID-19 pandemic waves, and rainfall had nothing to do with COVID-19 during any of the three waves. So, according to the results of this study, the Indian government should take strict steps to stop the spread of the fourth wave of COVID-19 and any other diseases that can spread in urban areas based on the meteorological conditions.

Level of Exposure of Populations to Atmospheric Pollution in Southern Benin

This study focuses on monitoring the exposure of populations in South Benin to atmospheric pollution. Thirteen (13) monitoring points were identified where the Air quality measurements were taken using autonomous electronic devices called “Air Quality Monitor”. Calibrated and turned on, the measuring device automatically determines the concentrations of carbon dioxide CO2, Total Organic Compounds, methanal (HCHO), particulate matter PM2.5 and PM10, temperature in degrees Fahrenheit and relative humidity (RH). Per site, air pollution levels are recorded for 5 minutes every 30 minutes from 7 a.m. to 6 p.m. After the analyses, it appears that the carbon dioxide CO2 contents vary from 400 to 1444 ppm with an average of 486.80 ± 184.3 ppm, the daily contents of Total Volatile Organic Compounds from 0.01 to 6 .91 mg/m3 with a daily average of 0.36 ± 0.65 mg/m3, aldehydes from 0.005 to 3 mg/m3 with an average of 0.05 ± 0.17 mg/m3, for particulate matter of diameters less than or equal to 2.5 μm (PM2.5) pollution levels vary between 5 and 173.8 μg/m3 with an average of 21.5 ± 17.62 μg/m3. On the other hand, for PM10, the contents vary from 5 to 449.6 μg/m3 with an average of 28.17 ± 31.74 μg/m3, the Air Quality Index (AQI) varies from 0, 3 to 243 with an average of 39 ± 33.16. From the results observed, it appears that the south-western part of South Benin is heavily polluted by CO2, Total Volatile Organic Compounds, PM10 and especially PM2.5 with the city of Cotonou at its head, in particular the Red Star crossroads, the Toyota crossroads and the crossroads after the friendship stadium. The impacts of this pollution could be significant on sensitive people such as the elderly, newborns and patients with acute and chronic respiratory illnesses.

Exposure to Automobile Pollutants and Sperm Quality among Mechanics in Brazzaville (Congo)

Atmospheric pollution is today at the heart of all debates because of its potentially harmful effects on the environment, the climate, and human health;it currently constitutes a real public health problem. However, the increase in infertility around the world has led scientists to look for a link between air pollution and fertility. This study consisted of evaluating the short-term influence of automobile pollution on the semen parameters of mechanics in Brazzaville. A cross-sectional, analytical, prospective study between two groups (G1, exposed people and G2, unexposed people) was carried out in Brazzaville on 228 patients, i.e., G1 with 76 subjects and G2 with 152 subjects, between June 2020 and September 2022, a period of 27 months, in order to evaluate, on the one hand, the quality of sperm in men exposed to automobile pollution according to WHO recommendations and, on the other hand, the quality of the air by a colorimetric method, punctual on a Dräger tube coupled with a Dräger Accuro pump. The concentrations of automotive pollutants measured (CO, CO2, NO2, SO2) during this study were all above the 2021 air quality standards required by WHO. These results made it possible to establish a statistically significant link between air pollution and abnormal spermogram parameters, notably mobility, count, and morphology for the spermocytogram. Exposures to automobile pollutants can influence sperm quality, which is consistent with the results of our study. We observed an alteration in the morphology, mobility, and concentration of spermatozoa.

Analysis of the Level of Pollutants in the ICS Location Area

The present study deals with the analysis of the level of pollution found in the area where the Chemical Industries of Senegal (ICS) is located. The results of various field surveys and physicochemical analyses show a high exposure of sulphur and phosphate. This study aims to define abatement strategies to reduce the effects of pollution in the area where ICS is located and mitigate the harmful impacts to the health of the population by creating a system to inform the population of the level of concentration of pollutants in the environment. This will allow the different targets to understand pollution, its manifestations and its effects on health and the environment, prevention, participation in the limitation of pollutant emissions and the formation of eco-citizens sensitive to the issue of environmental pollution of ICS.

Organic and Elemental Carbon in Atmospheric Fine Particulate Matter in an Animal Agriculture Intensive Area in North Carolina: Estimation of Secondary Organic Carbon Concentrations

Carbonaceous components contribute significant fraction of fine particulate matter (PM2.5). Study of organic carbon (OC) and elemental carbon (EC) in PM2.5 may lead to better understanding of secondary organic carbon (SOC) formation. This year-long (December 2008 to December 2009) field study was conducted in an animal agriculture intensive area in North Carolina of United States. Samples of PM2.5 were collected from five stations located in an egg production facility and its vicinities. Concentrations of OC/EC and thermograms were obtained using a thermal-optical carbon analyzer. Average levels of OC in the egg production house and at ambient stations were 42.7 μg/m3 and 3.26 - 3.47 μg/m3, respectively. Average levels of EC in the house and at ambient stations were 1.14 μg/m3 and 0.36 - 0.42 μg/m3, respectively. The OC to total carbon (TC) ratios at ambient stations exceeded 0.67, indicating a significant fraction of SOC presented in PM2.5. Principal factor analysis results suggested that possible major source of in-house PM2.5 was from poultry feed and possible major sources of ambient PM2.5 was from contributions of secondary inorganic and organic PM. Using the OC/EC primary ratio analysis method, ambient stations SOC fractions ranged from 68% to 87%. These findings suggested that SOC could appreciably contribute to total PM2.5 mass concentrations in this agriculture intensive area.

Diurnal Evolution of Urban Heat Island and Its Impact on Air Quality by Using Ground Observations (SAFAR) over New Delhi

The paper presents a study of urban heat island (UHI) intensity and its impact on air quality by using the System of Air Quality Forecasting and Research (SAFAR) network observations over Delhi during the clear sky month of December of 2013 and 2015. It is found that in the month of December 2013 and 2015 the UHI shows a peak in late evening around 20:00 hrs. The concentration of PM2.5 shows a bimodal peak in the month of December of both the years 2013 and 2015 which is due to the enhanced anthropogenic activity during the traffic hours. The formation of UHI during the late evening traffic hours is due to the enhancement in the concentration of PM2.5 due to the enhanced anthropogenic activity with higher ground heat flux and lower PBLH and wind speed which leads to both the years 2013 and 2015 during the month of December. It is also found that UHI intensity shows a positive correlation (r = 0.57) with PM2.5 concentration and a negative correlation (r = -0.40) with wind speed and the PM2.5 concentration also shows a negative correlation (r = -0.57) with wind speed during December 2013. Whereas during December 2015 it has found that UHI intensity has a positive correlation (r = 0.65) with PM2.5 concentration and a negative correlation (r = -0.45) with wind speed and the PM2.5 concentration also shows a negative correlation (r = -0.57) with wind speed.

Evaluating Mitigation Plans over Traffic Sector to Improve NO₂Levels in Andalusia (Spain) Using a Regional-Local Scale Photochemical Modelling System

In this contribution, we present an evaluation of different mitigation plans to improve NO2 levels in Andalusia, a region in the south of Spain. Specifically, we consider four possible mitigation plans: the effects over NO2 concentration of apply changes in the distribution of Vehicles Park;the effect of realize traffic restrictions (affecting to the density flow of vehicles) over highways and main roads;the effect of replacement of diesel use by natural gas in urban areas;and the effect of applying new velocity limits to access to urban areas. A sophisticated air quality modelling (AQM) system has been used to evaluate these mitigation plans. AQM implemented is composed on WRF meteorological model, an emission model created by the authors and CMAQ photochemical model. AQM analyzes mitigation plans during fifteen episodes of 2011 where NO2 levels were the highest of the year;so we analyze the effect of mitigation plans in worst conditions. Results provided by the AQM system show that: 1-h maximum daily NO2 is reduced to 10μg.m-3 near circulation roads when traffic restrictions and velocity limits plans are applied (NOx emissions are reduced in 9% - 15%);1-h maximum daily NO2 is reduced to 12 μg.m-3 affecting all municipalities when changes in the distribution of Vehicles Park are applied (NOx emissions are reduced in 25% - 26%);and the replacement of fuel of urban buses does not affect considerably NO2 levels.

A Review of Air Pollution and Control in Hebei Province, China

Hebei is one of the most air polluted provinces in China. According to the Ministry of Environmental Protection (MEP) for the severe fog-haze month of Jan. 2013, seven of the top ten most polluted cities in China are located in Hebei Province. In this study, the air pollution history and status of the Hebei Province are reviewed and discussed, using the governmental published Air Pollution Index (API), the academic observations by various scientific research groups and the long-term statistics of visibility and haze frequencies. It is found that within the Hebei Province, the air pollution in the southern cities is much more severe than the northern cities. Particulate matter (PM) is undoubtedly the major air pollutant, sulfur dioxide (SO2) and nitrogen oxides (NOX) pollutions are also unnegligible. Ozone (O3) pollution in larger cities, such as Shijiazhuang, is significant. Air pollution control history from 1998 is discussed as well. Although Hebei Province has made a great effort on air quality, the pollutant emissions, such as SO2 and fly ash, showed a notable increase in 2001 to 2006. However, after 2006 the emissions started to decrease due to the strict implementation of the national 11th Five Year Plan (FYP). In addition, regional jointly air pollution control and prevention strategies are expected in the future to substantially change the severe air pollution status in Hebei Province.

Prenatal Exposure to Polycyclic Aromatic Hydrocarbons and Birth Weight in China

Adverse birth outcomes are a leading cause of mortality in children in China, but the environmental influences of these conditions remain largely unexplained in this population. We aimed to evaluate the levels of polycyclic aromatic hydrocarbons (PAHs) in Chinese pregnant women and their newborns, and to examine the association between levels of PAHs and infant birth weight. We conducted a cross-sectional study including 81 pairs of mothers and newborns from four hospitals in four different cities in China. High Performance Liquid Chromatography was used to measure the concentration of nine PAHs in maternal and cord blood and multiple linear regression analyses were used to evaluate the associations of these PAHs with infant birth weight. Anthracene (ANT) had the highest average concentration and detection rate (geometric mean = 69.54 ng/g and 76.5%, respectively) in maternal serum samples, while fluoranthene (FLT) had the highest concentration and detection rate (geometric mean = 68.4 ng/g and 50.6%, respectively) in the cord blood. Most of the measured PAHs in maternal serum and three PAHs in cord blood were inversely but non-significantly associated with birth weight. The strongest associations were observed for higher concentrations of benzo (a) pyrene (BaP) in maternal serum (230.7 g decrease for levels > median vs. < LOD;p = 0.151) and for ANT in cord blood (153.1 g decrease for levels < median vs. < LOD;p = 0.208). Ant and FLT were the predominant PAHs in the maternal and cord blood serum. Serum concentrations of several measured PAHs were associated with a decreased birth weight, although not significantly, suggesting that further studies with larger sample sizes are needed to validate our findings.

Spatiotemporal Analysis of the Effect of Ozone and Fine Particulate on CVD Emergency Room Visits in Harris County, Texas

Researchers have reported statistically significant associations between air pollutants and cardiovascular disease (CVD). However, few studies have investigated the acute cardiovascular effects of joint exposure to Ozone (O3) and fine particulate matter (PM2.5) in a location so distinctive like Harris County. The objective of this study was to investigate the association between the joint exposure to O3 and PM2.5, and emergency room diagnosis of CVD, in Harris County, Texas. Data used include all emergency room (ER) visits, and O3 and PM2.5 levels in the same years. Logistic regression modeled the effect of temperature, relative humidity, wind speed, wind, ozone, and fine particulate matter, averaged by day. Three models were estimated for all visits, visits during the months of April and September of 2005 and 2009, and for visits for patients from zip codes that are close to monitoring stations. A 1 μg/m3 increase in PM2.5 was associated with a 0.4% increase in the odds of cardiovascular disease emergency room diagnoses on the same day

Regional Air Quality of the Nigeria’s Niger Delta

There is no systematic attempt to evaluate the air quality of any settlement in the Niger Delta region over a long period. Records of air quality data for this study were generated through secondary sources from impact assessment of facilities aimed at implementing air quality regulations on the environment. Suspended particulate matter in the region’s atmosphere ranged from 40 mg/m3 in Brass to 98 mg/m3 in Port Harcourt. Carbon monoxide concentrations were highest in Mbiama (191 mg/m3). Nitrogen dioxide concentration was highest in Bonny (187 mg/m3), and sulphur dioxide concentrations ranged from 19 mg/m3 in Ukwugba to 90 mg/m3 in Port Harcourt. Total hydrocarbon ranged from 78 mg/m3 in Odukpani to 192 mg/m3 in Nchia. Carbon dioxide ranged from 400 ppm in Buguma to 450 ppm in Port Harcourt. The most abundant of the VOCs is benzene and toluene. Ethylene was detected only in one station at concentration of 0.1 mg/m3 which was negligible. The most abundant of the metals was zinc, which was present at above 2 mg/m3 in most of the study settlements. In remote settlements like Buguma and Emuoha, some of the metals were not detected at all. In some instances, short-term limits for the pollutants exceeded WHO standards. The need for stakeholders in the region to articulate initiatives that support quality environmental practices was emphasized as laws pertaining to air quality regulations which are weak and less enforceable.