Variations In Pollution Levels Research Articles

Could persistent organic pollutants affect future generations of sea turtles by maternal transfer? First results for Caretta caretta nests along the North-Western coast of Italy

Since 2013, loggerhead sea turtle (Caretta caretta, Linnaeus 1758) nesting has been observed further north along the Italian coast, reaching the Tuscan coast (NW Mediterranean Sea). The four nesting events that occurred in Tuscany in the summer of 2019 spurred the scientific community to monitor these occurrences more carefully, following them from egg deposition to hatching. This provided an opportunity to collect samples for conducting multidisciplinary investigations, including the toxicological investigations of the biological material collected from the four nests. The aim of this study was to conduct an initial assessment of persistent organic pollutants in the eggs laid in Tuscany, aiming to establish a baseline on this topic for subsequent nesting events that have occurred until today. Organochlorine compounds (OCs), specifically polychlorinated biphenyls (PCBs), dichlorodiphenyltrichloroethane (DDT) and its metabolites, and hexachlorobenzene (HCB), were analyzed and detected in unhatched eggs, embryos, and chorio-allantoic membranes (CAMs). OCs were detected in all samples, with PCBs > DDTs ≫ HCB. A significant spatial variation in pollutant levels and profiles among sea turtle nesting locations was found. Embryos showed higher levels of contamination than egg contents regardless of the developmental stages. Depth of the laying chamber and egg mass were not significant factors in OC bioaccumulation. For the first time in the Mediterranean Sea, this study assessed the role of CAM in the transfer of contaminants to the embryo. Overall, the OC levels found were lower compared to the results from other studies conducted worldwide on loggerhead sea turtle biological material. This was the first assessment of nest biological material for the North-Western coast of Italy.

Ubiquitous occurrence of p-Phenylenediamine (PPD) antioxidants and PPD-quinones in fresh atmospheric snow and their amplification effects on associated aqueous contamination

p-Phenylenediamine (PPD) antioxidants are heavily used for protection of commercial rubber products (e.g., vehicle tire), resulting in their widespread contamination in ecosystem. PPD-quinones (PPDQs), the toxic quinone derivatives of PPDs, are also discovered as novel environmental pollutants. However, the contamination characteristics of PPDs/PPDQs in fresh atmospheric snow (without deposition on the Earth surface) have seldom been studied. This work first reports the broad distributions of PPDs and PPDQs in fresh atmospheric snow collected from seven Chinese urban areas. Individual median values of detected concentrations were in the ranges of 0.4 to 260 pg g−1 (PPDs) and 0.7 to 104 pg g−1 (PPDQs). The concentration deviation by long-term deposition on the ground was eliminated. In most sampling regions, wearing of vehicle rubber tires was possibly responsible for spatial-dependent PPDs’ pollution level variations, and high concentrations of PPDs promoted PPDQs’ formation in snow from atmosphere. Yet, excessive O3 may further oxidize and reduce PPDQs in atmospheric fresh snow from Zhengzhou, which is different from previous research. Furthermore, snowfall was noticed might amplify concentrations of three PPDs and PPDQs in an inland lake, which possibly worsen corresponding pollution in water system. Current study elucidates the potential impacts of snow-bound PPDs/PPDQs on ecosystems should not be underestimated.

The mortality impact of fine particulate matter in China: Evidence from trade shocks

We use county-level panel data to estimate the long-run effect of fine particulate matter (PM 2.5 ) pollution on mortality in China. Our causal inference relies on changes in local pollution via wind transport and demand shocks of Chinese products from export destinations amid the global economic crisis during the late 2000s. We find an economically and statistically significant impact of long-term exposure to PM 2.5 on cardiovascular and respiratory mortality, and the effect is the largest for those 65 years and older. Using the substantial variation in pollution levels both across time and space in China, we provide evidence of a concave dose-response function, with diminishing marginal mortality impacts of pollution at levels beyond those in developed nations.

Spatial Analyses of Air Pollutants Concentration around the Warri Refining and Petrochemical Company (WRPC), Delta State, Nigeria

This study investigates air pollution from the Warri Refining and Petrochemical Company (WRPC) of DeltaSate, Nigeria with the intent of determining the variations in pollution levels associated with increasingdistance from the refinery. The following pollutant gases: Carbon monoxide (CO), Volatile organiccompounds (VOC), Hydrogen sulphide (H2S), Nitrogen dioxide (NO2), Sulphur dioxide (SO2) andParticulate Matter (PM2.5 and PM10), were monitored intermittently with the use of digital hand-heldprobes, at sampling points located between 1,500 meters to 16,000 meters from WRPC. Air sampling wascarried out on a weekly basis, for a duration of one (1) year. The average annual concentration of CO, VOC,H2S, NO2, SO2, PM2.5 and PM10 measured were 0.2543 ppm, 4.4922 mg/m3, 0.0004 ppm, 0.0063 ppm,0.5263 ppm, 36.3825 μg/m3, 91.7346 μg/m3 respectively. The results of the spatial analyses of air pollutantsshow that concentrations of VOC, NO2, PM2.5, and PM10 shared a significant inverse relationship withdistance (p values 0.00 ≤.0.05). The study suggests a minimum of 10,250 meters radial extent of buffersaround WRPC, as a long-term strategy in reducing exposure of residents to air pollution. Short-termstrategies include enforcement of legislation reducing/banning emissions from industries and bush fires, useof alternative eco-friendly technologies and energy sources, tree planting, revamp of the hydroelectricitypower sector and general sanitization of the environment.

Satellite Observation of Spatio-temporal Variations in Nitrogen Dioxide over West Africa and Implications for Regional Air Quality.

Background.Nitrogen dioxide (NO2) is known to affect human health, causing heart and cardiovascular diseases, and it has been shown that locations with long term NO2 pollution recorded a high number of fatalities due to the COVID-19 pandemic. There are no ground stations monitoring emissions of NO2 over West Africa. The present study aimed to use satellite observations to examine pollutant trends over this region.Objective.To examine the trend of NO2 over the entire West Africa sub region in relationship to contributions to environmental emissions using satellite-derived data. This enables the assessment of West Africa regional air pollution hot spots in relationship to enhancing atmospheric factors. The results from this study will also be useful guidance for setting air quality standards for air pollution controls to minimize health hazards.Methods.The present study examined thirteen years of average monthly values of nitrogen dioxide (NO2) to determine the spatio-temporal variation of this pollutant over West Africa. Satellite data for NO2 between 2005 and 2017 were used to determine the variation in pollution levels over West Africa. Correlations between NO2 and meteorological variables (wind speed, rainfall and air temperature) were obtained to explain the influence of West African weather on the region's pollution accumulation.Results.The present study observed that NO2 concentrations varied from place to place and from season to season. Nitrogen dioxide concentrations during the dry season were higher (sometimes 200% higher) than values observed in the wet season which ranged between 0.5 and 6×1015 molec/cm2. Nitrogen dioxide north-south oscillation during the course of a year is largely controlled by the inter-tropical discontinuity (ITD) zone as high concentrations of NO2 are found in the vicinity of the ITD where wind speeds and horizontal vorticity approaches zero. Correlation analysis between NO2 and some atmospheric variables indicated NO2 concentrations are well influenced by atmospheric variables showing bipolar signals depending on the season. An increasing trend of NO2 was also found over selected cities of the region. This indicated that regional air quality is gradually deteriorating.Conclusions.The implications of worsening regional air quality were examined in the light of the prevailing COVID-19 pandemic. The dominant atmospheric factor determining pollution episodes in the region is the inter-tropical discontinuity line which marks the meeting point between the two wind regimes over the region. Densely populated areas are characteristically prone to elevated pollution and have experienced high fatalities during the COVID-19 pandemic.Competing Interests.The authors declare no competing financial interests.

Identification of air pollution hotspots in urban areas - An innovative approach using monitored concentrations data

Critical assessment of spatio-temporal variations in pollution levels is a crucial step for identifying and prioritizing air pollution hotspots (APH) in urban areas. There is no universally accepted methodology for defining and delineating air pollution hotspot which can be source-specific, pollutant-specific and time-specific. The present research article is an attempt to develop a protocol for identifying APH for any pollutant within a city where-in three criteria-based innovative methodology has been derived. The three criteria are frequency of exceedance (% of days), scale of exceedance and consistency in exceedance (consecutive number of days) to the specified standards that need to be met continuously for at least three years. The suggested methodology has been applied on a three-year database (2018–2021) of 37 continuous ambient air quality stations to identify PM2.5 specific APH. The analysis indicates 11 APH in April, 9 in May, 2 in June and almost the entire city during the October–February months. Given prioritization of implementation of control actions, the identified APH during summer has been further physically examined to map source activity types and their suitability for ambient air quality monitoring stations as per the guidelines. The APH can be the priority areas for the implementation of control actions by urban local bodies. The management of air pollution at these priority areas would be more effective instead of city-scale management practice, which is difficult to implement and monitor.

PM2.5 as a major predictor of COVID-19 basic reproduction number in the USA

Many studies have proposed a relationship between COVID-19 transmissibility and ambient pollution levels. However, a major limitation in establishing such associations is to adequately account for complex disease dynamics, influenced by e.g. significant differences in control measures and testing policies. Another difficulty is appropriately controlling the effects of other potentially important factors, due to both their mutual correlations and a limited dataset. To overcome these difficulties, we will here use the basic reproduction number (R0) that we estimate for USA states using non-linear dynamics methods. To account for a large number of predictors (many of which are mutually strongly correlated), combined with a limited dataset, we employ machine-learning methods. Specifically, to reduce dimensionality without complicating the variable interpretation, we employ Principal Component Analysis on subsets of mutually related (and correlated) predictors. Methods that allow feature (predictor) selection, and ranking their importance, are then used, including both linear regressions with regularization and feature selection (Lasso and Elastic Net) and non-parametric methods based on ensembles of weak-learners (Random Forest and Gradient Boost). Through these substantially different approaches, we robustly obtain that PM2.5 is a major predictor of R0 in USA states, with corrections from factors such as other pollutants, prosperity measures, population density, chronic disease levels, and possibly racial composition. As a rough magnitude estimate, we obtain that a relative change in R0, with variations in pollution levels observed in the USA, is typically ~30%, which further underscores the importance of pollution in COVID-19 transmissibility.

Fine-Scale Air Pollution Models for Epidemiologic Research: Insights From Approaches Developed in the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air).

Epidemiological studies of short- and long-term health impacts of ambient air pollutants require accurate exposure estimates. We describe the evolution in exposure assessment and assignment in air pollution epidemiology, with a focus on spatiotemporal techniques first developed to meet the needs of the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Initially designed to capture the substantial variation in pollutant levels and potential health impacts that can occur over small spatial and temporal scales in metropolitan areas, these methods have now matured to permit fine-scale exposure characterization across the contiguous USA and can be used for understanding long- and short-term health effects of exposure across the lifespan. For context, we highlight how the MESA Air models compare to other available exposure models. Newer model-based exposure assessment techniques provide predictions of pollutant concentrations with fine spatial and temporal resolution. These validated models can predict concentrations of several pollutants, including particulate matter less than 2.5 μm in diameter (PM2.5), oxides of nitrogen, and ozone, at specific locations (such as at residential addresses) over short time intervals (such as 2 weeks) across the contiguous USA between 1980 and the present. Advances in statistical methods, incorporation of supplemental pollutant monitoring campaigns, improved geographic information systems, and integration of more complete satellite and chemical transport model outputs have contributed to the increasing validity and refined spatiotemporal spans of available models. Modern models for predicting levels of outdoor concentrations of air pollutants can explain a substantial amount of the spatiotemporal variation in observations and are being used to provide critical insights into effects of air pollutants on the prevalence, incidence, progression, and prognosis of diseases across the lifespan. Additional enhancements in model inputs and model design, such as incorporation of better traffic data, novel monitoring platforms, and deployment of machine learning techniques, will allow even further improvements in the performance of pollutant prediction models.

Assessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California.

Background:Air pollution-attributable disease burdens reported at global, country, state, or county levels mask potential smaller-scale geographic heterogeneity driven by variation in pollution levels and disease rates. Capturing within-city variation in air pollution health impacts is now possible with high-resolution pollutant concentrations.Objectives:We quantified neighborhood-level variation in air pollution health risks, comparing results from highly spatially resolved pollutant and disease rate data sets available for the Bay Area, California.Methods:We estimated mortality and morbidity attributable to nitrogen dioxide (), black carbon (BC), and fine particulate matter [PM in aerodynamic diameter ()] using epidemiologically derived health impact functions. We compared geographic distributions of pollution-attributable risk estimates using concentrations from a) mobile monitoring of and BC; and b) models predicting annual , BC and concentrations from land-use variables and satellite observations. We also compared results using county vs. census block group (CBG) disease rates.Results:Estimated pollution-attributable deaths per 100,000 people at the grid-cell level ranged across the Bay Area by a factor of 38, 4, and 5 for [ (95% CI: 9, 50)], BC [ (95% CI: 1, 2)], and , [ (95% CI: 33, 64)]. Applying concentrations from mobile monitoring and land-use regression (LUR) models in Oakland neighborhoods yielded similar spatial patterns of estimated grid-cell–level mortality rates. Mobile monitoring concentrations captured more heterogeneity [mobile monitoring (95% CI: 19, 107) deaths per 100,000 people; (95% CI: 30, 167)]. Using CBG-level disease rates instead of county-level disease rates resulted in 15% larger attributable mortality rates for both and , with more spatial heterogeneity at the grid-cell–level [ CBG deaths per 100,000 people (95% CI: 12, 68); (95% CI: 11, 64); (95% CI: 40, 77); and (95% CI: 37, 71)].Discussion:Air pollutant-attributable health burdens varied substantially between neighborhoods, driven by spatial variation in pollutant concentrations and disease rates. https://doi.org/10.1289/EHP7679

High-resolution spatiotemporal measurement of air and environmental noise pollution in Sub-Saharan African cities: Pathways to Equitable Health Cities Study protocol for Accra, Ghana

IntroductionAir and noise pollution are emerging environmental health hazards in African cities, with potentially complex spatial and temporal patterns. Limited local data are a barrier to the formulation and evaluation...

Predicting Fine Particulate Matter (PM2.5) in the Greater London Area: An Ensemble Approach using Machine Learning Methods

Estimating air pollution exposure has long been a challenge for environmental health researchers. Technological advances and novel machine learning methods have allowed us to increase the geographic range and accuracy of exposure models, making them a valuable tool in conducting health studies and identifying hotspots of pollution. Here, we have created a prediction model for daily PM2.5 levels in the Greater London area from 1st January 2005 to 31st December 2013 using an ensemble machine learning approach incorporating satellite aerosol optical depth (AOD), land use, and meteorological data. The predictions were made on a 1 km × 1 km scale over 3960 grid cells. The ensemble included predictions from three different machine learners: a random forest (RF), a gradient boosting machine (GBM), and a k-nearest neighbor (KNN) approach. Our ensemble model performed very well, with a ten-fold cross-validated R2 of 0.828. Of the three machine learners, the random forest outperformed the GBM and KNN. Our model was particularly adept at predicting day-to-day changes in PM2.5 levels with an out-of-sample temporal R2 of 0.882. However, its ability to predict spatial variability was weaker, with a R2 of 0.396. We believe this to be due to the smaller spatial variation in pollutant levels in this area.

Residential exposure to outdoor air pollution and adult lung function, with focus on small airway obstruction

Although a growing body of evidence suggests that chronic exposure to outdoor air pollution is linked to a decline in lung function, data on flow at low lung volumes that may be more specific of small airway obstruction are still scarce. We aimed to study the associations between residential exposure to air pollution and lung function, with specific focus on small airways obstruction.We assessed 2995 French participants (aged between 40 and 65) in the ELISABET cross-sectional survey. Residential exposures to nitrogen dioxide (NO2), particulate matter with a diameter <10 μm (PM10) and sulphur dioxide (SO2) were assessed. The spirometric parameters were forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), and forced expiratory flow between 25% and 75% of FVC (FEF25-75) and at 75% of FVC (FEF75). Coefficients in linear regression models were expressed as the z-score [95% confidence interval] for an increment of 5 μg/m3 in NO2 and 2 μg/m3 in PM10 and SO2.NO2 was associated with significantly lower values of FEV1 (−0.10 [-0.15;-0.05]), FVC (−0.06 [-0.11;-0.02]), FEV1/FVC (−0.07 [-0.11;-0.03]), FEF25-75 (−0.09 [-0.14;-0.05]) and FEF75 (−0.08 [-0.12;-0.04]). PM10 was associated with significantly lower values of FEV1 (−0.10 [-0.15;-0.04]), FVC (−0.06 [-0.11;-0.01]), FEV1/FVC (−0.06 [‒0.11;-0.01]), FEF25-75 (−0.08 [-0.13;-0.03]) and FEF75 (−0.08 [-0.12;-0.04]). SO2 was associated with significantly lower values of FEV1 (−0.09 [-0.16;-0.02]), FEV1/FVC (−0.07 [-0.13;-0.01]), FEF25-75 (−0.09 [-0.15;-0.02]) and FEF75 (−0.08 [-0.14;-0.03]) but not FVC (−0.05 [-0.11; 0.009]).Even though spatial variations in pollutant levels were low, residential exposure to outdoor air pollution was associated with lower lung function, including lower FEF25-75 and FEF75 suggesting small airway obstruction.

Short-term spatiotemporal biomarker changes in oysters transplanted to an anthropized estuary in Southern Brazil

Estuarine ecosystems are increasingly being affected by pollution caused by anthropogenic activities. In this study, Crassostrea gasar oysters were transplanted and maintained for seven days at three sites (S1, S2, and S3) in the Laguna Estuarine System (LES)—situated in southern Brazil—that has been exposed to multiple anthropic stresses. On the basis of the concentrations of metal and organic pollutants in oysters, we identified marked spatial variations in pollutant levels, with S3 showing the highest concentration of Ag, Fe, Ni, Zn, and total polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and linear alkylbenzenes (LABs), followed by S2 and S1. Along with the concentrations of pollutants, a set of biomarkers was analyzed. Oysters maintained at S3 showed enhanced protective defenses in gills, as observed by the increased levels of superoxide dismutase (SOD-like) and heat shock protein 90 (HSP90-like) transcripts and catalase (CAT) activity, concomitant with reduced lipid peroxidation (MDA) levels. Decreased antioxidant activities together with increased MDA levels are indicative of the digestive gland being more susceptible to pollutant-induced oxidative damage. Oysters transplanted into LES showed lower levels of cytochrome P450 transcripts (CYP356A1-like and CYP2AU1), and decreased glutathione S-transferase (GST) enzyme activity, suggesting lower biotransformation capacity. By integrating information regarding the concentration of metal and organic pollutants with that of molecular as well as biochemical biomarkers, our study provides novel insights into pollutant exposure and the potential biological impacts of such exposure on estuarine organisms in southern Brazil.

As the Wind Blows: The Effects of Long-Term Exposure to Air Pollution on Mortality.

There is strong evidence that short-run fluctuations in air pollution negatively impact infant health and contemporaneous adult health, but there is less evidence on the causal link between long-term exposure to air pollution and increased adult mortality. This project estimates the impact of long-term exposure to air pollution on mortality by leveraging quasi-random variation in pollution levels generated by wind patterns near major highways. I combine geocoded data on the residence of every decedent in Los Angeles over three years, high-frequency wind data, and Census short form data. Using these data, I estimate the effect of downwind exposure to highway-generated pollutants on the age-specific mortality rate by using orientation to the nearest major highway as an instrument for pollution exposure. I find that doubling the percentage of time spent downwind of a highway increases mortality among individuals 75 or older by 3.8%-6.5%. These estimates are robust and imply significant loss of life years.

Ambient air quality and spatio-temporal patterns of cardiovascular emergency department visits

BackgroundAir pollutants have been associated with various adverse health effects, including increased rates of hospital admissions and emergency room visits. Although numerous time-series studies and case-crossover studies have estimated associations between day-to-day variation in pollutant levels and mortality/morbidity records, studies on geographic variations in emergency department use and the spatial effects in their associations with air pollution exposure are rare.MethodsWe focused on the elderly who visited emergency room for cardiovascular related disease (CVD) in 2011. Using spatially and temporally resolved multi-pollutant exposures, we investigated the effect of short-term exposures to ambient air pollution on emergency department utilization. We developed two statistical models with and without spatial random effects within a hierarchical Bayesian framework to capture the spatial heterogeneity and spatial autocorrelation remaining in emergency department utilization.ResultsAlthough the cardiovascular effect of spatially homogeneous pollutants, such as PM2.5 and ozone, was unchanged, we found the cardiovascular effect of NO_2 was pronounced after accounting for the spatially correlated structure in emergency department utilization. We also identified areas with high ED utilization for CVD among the elderly and assessed the uncertainty associated with risk estimates.ConclusionsWe assessed the short-term effect of multi-pollutants on cardiovascular risk of the elderly and demonstrated the use of community multiscale air quality model-derived spatially and temporally resolved multi-pollutant exposures to an epidemiological study. Our results indicate that NO_2 was significantly associated with the elevated ED utilization for CVD among the elderly.

Walk the Less Trodden Path, or Rather, the Less Driven: Mapping Pedestrian Paths and Local Air Pollution for Selective Walking in Cleaner Air ( breakout presentation )

When we walk outdoors, we breathe ambient air. Walking in an urban environment exposes us to varying levels of air pollution. Yet, air quality is only emerging as a dimension of walkability. Disregarding air quality when assessing walkability is just the tip of the iceberg: we tend to be unaware of the variability of air pollution over space, even though we are aware of the meteorological variability within our city. Yet, variations in pollution levels within urban environments can be large. Just think about Calgary: a metropolitan area over 800 km 2 , with a 300 m elevation range, strong winds, and localized pollution sources. Truly, where we walk can make a difference in the quality of the air we breathe. Further, several traffic-related pollutants exhibit a spatial pattern with pollutant concentration declining rapidly as distance from roads increases; therefore, walking within a few hundred meters of a major road leads to higher pollution exposure than if we were to walk further away from the road. Indeed, air pollution is measured regularly and frequently over time, but irregularly and sparsely over space, failing to capture its spatial variation. Over the last several years, in collaboration with Health Canada, our group has conducted spatially detailed air monitoring campaigns, deploying up to 100 monitors within the urban area of Calgary. We subsequently developed land use regression models, which yield reliable spatially detailed estimates of air pollution, e.g., at the postal code level. We further integrated our spatial estimates with the temporal measurements of the regulatory network, which yielded air pollution estimates at the postal code level on a monthly base; therefore, we can provide integrated estimates of spatial and seasonal variation in air pollution levels. We are integrating our estimates with walkability assessments, using Calgary as a pilot study. We further analyzed pollution levels over the city’s existing extensive pathway network. The outcome of this study is a map reporting pedestrian (and bicycle) paths, with associated levels of pollution in each season. The map shall help guiding ‘where to walk’ choices, promoting walking (and cycling) on pathways where pollution levels are lower.

Influence of urban vegetation on air pollution and noise exposure – A case study in Gothenburg, Sweden

Air pollution levels (NO2, PAHs, O3) were investigated, before (BLE) and after (ALE) leaf emergence, in the urban landscape of Gothenburg, Sweden. The aims were to study the 1) spatial and temporal variation in pollution levels between urban green areas, 2) effect of urban vegetation on air pollution levels at the same distance from a major emission source (traffic route), 3) improvement of urban air quality in urban parks compared to adjacent sites near traffic, 4) correlation between air pollution and noise in a park. O3 varied little over the urban landscape. NO2 varied strongly and was higher in situations strongly influenced by traffic. Four PAH variables were included: total PAH, total particle-bound PAH, the quantitatively important gaseous phenanthrene and the highly toxic particle-bound benzo(a)pyrene. The variation of PAHs was similar to NO2, but for certain PAHs the difference between highly and less polluted sites was larger than for NO2. At a vegetated site, NO2 and particulate PAH levels were lower than at a non-vegetated site at a certain distance from a busy traffic route. This effect was significantly larger ALE compared to BLE for NO2, indicating green leaf area to be highly significant factor for air quality improvement. For particulate PAHs, the effect was similar BLE and ALE, indicating that tree bark and branches also could be an important factor in reducing air pollution. Parks represented considerably cleaner local environments (park effect), which is likely to be a consequence of both a dilution (distance effect) and deposition. Noise and air pollution (NO2 and PAH) levels were strongly correlated. Comparison of noise levels BLE and ALE also showed that the presence of leaves significantly reduced noise levels. Our results are evidence that urban green spaces are beneficial for urban environmental quality, which is important to consider in urban planning.

Heavy Metals, Phosphates and Nitrates Levels in Vegetables: A Case Study of Kitale Municipality, Trans-Nzoia County, Kenya

Trans-Nzoia county covers an area 2,469.90 Km2 with a total Population of 818,757 (Male 50%, Female 50%) and a poverty index of 5.2% (KNBS, 2011). The main economic activities in Tran-Nzoia county include horticulture, large and small scale maize farming , wheat farming, Tea, Coffee and fish farming. Effluents released from sewage treatment plant contain toxic metal pollutant whose uptake by vegetables is governed by their availability and concentration in the soil. Therefore such vegetables may accumulate pollutants in excessive amounts and this can ultimately, adversely harm humans and other species that depend on such crops for food. An evaluation of the variation of pollutant levels in vegetables and soil samples Bidii and Taito area in Kitale has been done. Two vegetable samples of were freshly harvested from two farms within the vicinity of Kitale municipality, Trans Nzoia county. The concentration of heavy metals which include, Pb, Cu, Zn and Cr, were determined using Atomic Absorption Spectroscopy (AAS). Levels of some anions (nitrate and phosphate) were determined using Uv-Vis Spectrophotometer. The heavy metal amounts detected in soils ranged between 0.544-0.396 mg/kg of Cu, 6.288-1.093 mg/kg of Zn, 4.588-0.988 mg/kg of Pb and 1.167-0.297 mg/kg of Cr. In the case of vegetables, they ranged from 1.110-0.224 mg/kg of Cu, 1.365-0.101 mg/kg of Zn, 1.842-0.859 mg/kg of Pb and 0.938-0.020 mg/kg of Cr. The concentrations of the anions ranged between 2.06-1.88 mg/kg nitrate and 4.99-4.66 mg/kg of phosphate in soils and 3.202-2.176 mg/kg of nitrate and 3.342-2.839 mg/kg of phosphate in vegetables. Overall, the present study showed that the heavy metal levels in soil were below the WHO permissible levels. However, the levels of Pb and Cr in vegetables were found to be above the WHO permissible levels. The high values might be attributed to the use of untreated effluents from the sewage treatment plant by farmers for the irrigation of these vegetables. Thus, the high values of these trace metals in the vegetable samples could put the consumers of these vegetables at health risk. Further works should be carried out in the soil samples were the vegetables are grown.

Investigation of Impacts of Mining and Transportation Activities on Ambient Air Quality of Dhanbad City

The present study was intended to emphasize the assessment of ambient air quality of Dhanbad city with respect to PM10, PM2.5, SO2 and NOX concentrations, in order to investigate the impact of mining and transportation activities. From the monitoring and analysis at four selected monitoring stations during winter and summer seasons, significant spatial variation in pollutant (PM10, PM2.5, SO2 and NOX) concentrations is quite evident. The concentrations of PM10 were observed highest in mining area (at Dhansar PS; 291 µg/m3), whereas the PM2.5 the concentrations were observed higher along traffic routes (especially, at Bank More; 218 µg/m3). Higher concentratios of PM10 in mining area indicates the substantial impact of dust emanated from mining and associated activities on air quality. Whereas, the higher PM2.5 concentration along the transportation routes shows the influence of transportation activities on the airshed of the area. The significant seasonal variation in pollution levels is also apparent, as the concentrations of every pollutant were observed higher during the winter, than the summer season, at all sites. The mean concentration levels of PM10 and PM2.5 were observed 267 µg/m3, 173 µg/m3 and 234 µg/m3, 108 µg/m3 during winter and summer seasons, respectively. From the calculated values of air quality index, it is evident that Dhansar PS and Bank More are most polluted sites and PM10 is the most alarming pollutant in the area under investigation.

The Relationship between Particulate Pollution Levels in Australian Cities, Meteorology, and Landscape Fire Activity Detected from MODIS Hotspots

Smoke from bushfires is an emerging issue for fire managers because of increasing evidence for its public health effects. Development of forecasting models to predict future pollution levels based on the relationship between bushfire activity and current pollution levels would be a useful management tool. As a first step, we use daily thermal anomalies detected by the MODIS Active Fire Product (referred to as “hotspots”), pollution concentrations, and meteorological data for the years 2002 to 2008, to examine the statistical relationship between fire activity in the landscapes and pollution levels around Perth and Sydney, two large Australian cities. Resultant models were statistically significant, but differed in their goodness of fit and the distance at which the strength of the relationship was strongest. For Sydney, a univariate model for hotspot activity within 100 km explained 24% of variation in pollution levels, and the best model including atmospheric variables explained 56% of variation. For Perth, the best radius was 400 km, explaining only 7% of variation, while the model including atmospheric variables explained 31% of the variation. Pollution was higher when the atmosphere was more stable and in the presence of on-shore winds, whereas there was no effect of wind blowing from the fires toward the pollution monitors. Our analysis shows there is a good prospect for developing region-specific forecasting tools combining hotspot fire activity with meteorological data.