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Dr. Kabindra Shakya
Department of Geography & the Environment, Villanova University, PA 19085, USA

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Research Keywords & Expertise

0 Environmental Health
0 Particulate Matter
0 Environmental Sustainability
0 Indoor and outdoor air quality
0 And environmental education

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Short Biography

Dr. Kabindra M. Shakya is an assistant professor at Department of Geography and the Environment at Villanova University. He completed his Ph.D. with the focus on atmospheric chemistry from Rice University, and he completed his postdoc with the focus in environmental health from the University of Massachusetts, Amherst. He has expertise in air pollution and environmental health. He has authored 25 peer-reviewed papers in this subject.

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Review
Published: 03 August 2021 in International Journal of Environmental Research and Public Health
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Global atmospheric warming leads to climate change that results in a cascade of events affecting human mortality directly and indirectly. The factors that influence climate change-related mortality within the peer-reviewed literature were examined using Whittemore and Knafl’s framework for an integrative review. Ninety-eight articles were included in the review from three databases—PubMed, Web of Science, and Scopus—with literature filtered by date, country, and keywords. Articles included in the review address human mortality related to climate change. The review yielded two broad themes in the literature that addressed the factors that influence climate change-related mortality. The broad themes are environmental changes, and social and demographic factors. The meteorological impacts of climate change yield a complex cascade of environmental and weather events that affect ambient temperatures, air quality, drought, wildfires, precipitation, and vector-, food-, and water-borne pathogens. The identified social and demographic factors were related to the social determinants of health. The environmental changes from climate change amplify the existing health determinants that influence mortality within the United States. Mortality data, national weather and natural disaster data, electronic medical records, and health care provider use of International Classification of Disease (ICD) 10 codes must be linked to identify climate change events to capture the full extent of climate change upon population health.

ACS Style

Ruth McDermott-Levy; Madeline Scolio; Kabindra Shakya; Caroline Moore. Factors That Influence Climate Change-Related Mortality in the United States: An Integrative Review. International Journal of Environmental Research and Public Health 2021, 18, 8220 .

AMA Style

Ruth McDermott-Levy, Madeline Scolio, Kabindra Shakya, Caroline Moore. Factors That Influence Climate Change-Related Mortality in the United States: An Integrative Review. International Journal of Environmental Research and Public Health. 2021; 18 (15):8220.

Chicago/Turabian Style

Ruth McDermott-Levy; Madeline Scolio; Kabindra Shakya; Caroline Moore. 2021. "Factors That Influence Climate Change-Related Mortality in the United States: An Integrative Review." International Journal of Environmental Research and Public Health 18, no. 15: 8220.

Preprint
Published: 05 May 2021
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Understanding the relationships between land cover/urban structure patterns and air pollutants is key to sustainable urban planning and development. In this study, we employ a mobile monitoring method to collect PM2.5 and BC data in the city of Philadelphia, PA during the summer of 2019 and apply the Structure of Urban Landscapes (STURLA) methodology to examine relationships between urban structure and atmospheric pollution. We find that, while PM2.5 and BC vary by STURLA class, many of the differences in pollutant concentrations between classes are not significant. However, we also find that the proportions in which STURLA components are present throughout the urban landscape can be used to predict urban air pollution. Among frequently sampled STURLA classes, gpl hosted the highest PM2.5 concentrations on average (16.60 ± 4.29 µg/m3), while tgbwp hosted the highest BC concentrations (2.31 ± 1.94 µg/m3). Furthermore, STURLA combined with machine learning modeling was able to correlate PM2.5 (R2= 0.68, RMSE 2.82 µg/m3) and BC (R2 = 0.64, RMSE 0.75 µg/m3) concentrations with the urban landscape and spatially interpolate concentrations where sampling did not take place. These results demonstrate the efficacy of the STURLA methodology in modeling relationships between air pollution and land cover/urban structure patterns.

ACS Style

Lucas Cummings; Justin Stewart; Peleg Kremer; Kabindra Shakya. Predicting Citywide Distribution of Air Pollution Using Mobile Monitoring and Three-dimensional Urban Structure. 2021, 1 .

AMA Style

Lucas Cummings, Justin Stewart, Peleg Kremer, Kabindra Shakya. Predicting Citywide Distribution of Air Pollution Using Mobile Monitoring and Three-dimensional Urban Structure. . 2021; ():1.

Chicago/Turabian Style

Lucas Cummings; Justin Stewart; Peleg Kremer; Kabindra Shakya. 2021. "Predicting Citywide Distribution of Air Pollution Using Mobile Monitoring and Three-dimensional Urban Structure." , no. : 1.

Original research article
Published: 04 May 2021 in Frontiers in Built Environment
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Urban air pollution poses a major threat to human health. Understanding where and when urban air pollutant concentrations peak is essential for effective air quality management and sustainable urban development. To this end, we implement a mobile monitoring methodology to determine the spatiotemporal distribution of particulate matter (PM) and black carbon (BC) throughout Philadelphia, Pennsylvania and use hot spot analysis and heatmaps to determine times and locations where pollutant concentrations are highest. Over the course of 12 days between June 27 and July 29, 2019, we measured air pollution concentrations continuously across two 150 mile (241.4 km) long routes. Average daily mean concentrations were 11.55 ± 5.34 μg/m3 (PM1), 13.48 ± 5.59 μg/m3 (PM2.5), 16.13 ± 5.80 μg/m3 (PM10), and 1.56 ± 0.39 μg/m3 (BC). We find that fine PM size fractions (PM2.5) constitute approximately 84% of PM10 and that BC comprises 11.6% of observed PM2.5. Air pollution hotspots across three size fractions of PM (PM1, PM2.5, and PM10) and BC had similar distributions throughout Philadelphia, but were most prevalent in the North Delaware, River Wards, and North planning districts. A plurality of detected hotspots found throughout the data collection period (30.19%) occurred between the hours of 8:00 AM–9:00 AM.

ACS Style

Lucas E. Cummings; Justin D. Stewart; Radley Reist; Kabindra M. Shakya; Peleg Kremer. Mobile Monitoring of Air Pollution Reveals Spatial and Temporal Variation in an Urban Landscape. Frontiers in Built Environment 2021, 7, 1 .

AMA Style

Lucas E. Cummings, Justin D. Stewart, Radley Reist, Kabindra M. Shakya, Peleg Kremer. Mobile Monitoring of Air Pollution Reveals Spatial and Temporal Variation in an Urban Landscape. Frontiers in Built Environment. 2021; 7 ():1.

Chicago/Turabian Style

Lucas E. Cummings; Justin D. Stewart; Radley Reist; Kabindra M. Shakya; Peleg Kremer. 2021. "Mobile Monitoring of Air Pollution Reveals Spatial and Temporal Variation in an Urban Landscape." Frontiers in Built Environment 7, no. : 1.

Journal article
Published: 30 March 2021 in Journal of Environmental Radioactivity
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Indoor radon poses one of the most significant environmental threats to public health as it is the second leading cause of lung cancer in the United States. Developing a more thorough understanding of the factors that affect radon concentrations is key for developing risk maps, identifying where testing should be a priority, and education about indoor radon exposure. The objectives of this study are to investigate seasonal and annual variation of indoor radon concentrations in Pennsylvania, USA from 1988 to 2018, to explore the hotspot areas for high indoor radon concentrations, and to analyze the association with various factors such as weather conditions, housing types, and floor levels. Based on a total of 1,808,294 radon tests conducted from 1988 to 2018, we found that 61% of the area (by zip codes), 557,869 tests conducted in the basement and 49,141 tests conducted on the ground floor in homes in Pennsylvania had higher radon levels than the U.S. EPA action level concentration of 148 Bq/m3 (equivalent to 4 pCi/L). Winter and fall had significantly higher indoor radon concentrations than summer and spring. Case studies conducted in Pittsburgh, Philadelphia, and Harrisburg showed that there was no significant correlation of daily temperature, precipitation, or relative humidity with indoor radon concentration on the day a radon test occurred.

ACS Style

Kyle R. Kellenbenz; Kabindra M. Shakya. Spatial and temporal variations in indoor radon concentrations in Pennsylvania, USA from 1988 to 2018. Journal of Environmental Radioactivity 2021, 233, 106594 .

AMA Style

Kyle R. Kellenbenz, Kabindra M. Shakya. Spatial and temporal variations in indoor radon concentrations in Pennsylvania, USA from 1988 to 2018. Journal of Environmental Radioactivity. 2021; 233 ():106594.

Chicago/Turabian Style

Kyle R. Kellenbenz; Kabindra M. Shakya. 2021. "Spatial and temporal variations in indoor radon concentrations in Pennsylvania, USA from 1988 to 2018." Journal of Environmental Radioactivity 233, no. : 106594.

Journal article
Published: 24 September 2020 in Building and Environment
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We present the first report of exposure to particulate matter by commuters at 12 underground subway stations in Philadelphia, Pennsylvania, USA. Particle measurements were taken during two springtime periods: March 4 to 9, 2018 and February 1 to April 12, 2019. Particle concentrations were variable across the subway stations and demonstrated high temporal variability (daily and yearly) at the underground subway stations with mean PM2.5 and PM10 concentrations of 54.6 ± 34.1 and 61.6 ± 38.9 μg/m3, respectively in 2018, and 45.1 ± 27.8 and 53.6 ± 32.7 μg/m3, respectively, in 2019. Compared to the mean aboveground street levels, the mean underground subway stations' PM2.5 levels were 5.1 times and 2.6 times higher in 2018 and 2019, respectively. The structure and ventilation system within the subway station likely influenced the variations among the subway stations. Subway stations designed with more direct access to the outside environment showed lower PM concentrations than the station with less direct access. In comparison to off-peak hours, higher levels were found during peak subway hours in 3 of the 6 subway stations for PM2.5, and 5 of the 8 subway stations for PM10. Overall, there is high level of PM exposure for commuters at Philadelphia subway stations. Based on the findings in this study, improvements in station design and preventive control measures are recommended to maximize ventilation and reduce PM exposure for Philadelphia's subway passengers and workers.

ACS Style

Kabindra M. Shakya; Alexander Saad; Alex Aharonian. Commuter exposure to particulate matter at underground subway stations in Philadelphia. Building and Environment 2020, 186, 107322 .

AMA Style

Kabindra M. Shakya, Alexander Saad, Alex Aharonian. Commuter exposure to particulate matter at underground subway stations in Philadelphia. Building and Environment. 2020; 186 ():107322.

Chicago/Turabian Style

Kabindra M. Shakya; Alexander Saad; Alex Aharonian. 2020. "Commuter exposure to particulate matter at underground subway stations in Philadelphia." Building and Environment 186, no. : 107322.

Preprint content
Published: 11 August 2020
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ACS Style

Lucas E Cummings; Justin Stewart; Radley Reist; Kabindra M Shakya; Peleg Kremer. Exploring the Spatiotemporal Variation of Air Pollution Throughout the Urban Landscape of Philadelphia, PA with Mobile Monitoring. 2020, 1 .

AMA Style

Lucas E Cummings, Justin Stewart, Radley Reist, Kabindra M Shakya, Peleg Kremer. Exploring the Spatiotemporal Variation of Air Pollution Throughout the Urban Landscape of Philadelphia, PA with Mobile Monitoring. . 2020; ():1.

Chicago/Turabian Style

Lucas E Cummings; Justin Stewart; Radley Reist; Kabindra M Shakya; Peleg Kremer. 2020. "Exploring the Spatiotemporal Variation of Air Pollution Throughout the Urban Landscape of Philadelphia, PA with Mobile Monitoring." , no. : 1.

Preprint content
Published: 19 June 2020
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Microbes are abundant inhabitants of the near-surface atmosphere in urban areas. The distribution of microbial communities may benefit or hinder human wellbeing and ecosystem function. Surveys of airborne microbial diversity are uncommon in both natural and built environments and those that investigate diversity are stationary in the city, thus missing continuous exposure to microbes that covary with three-dimensional urban structure. Individuals in cities are generally mobile and would be exposed to diverse urban structures outdoors and within indoor-transit systems in a day. We used mobile monitoring of microbial diversity and geographic information system spatial analysis, across Philadelphia, Pennsylvania, USA in outdoor and indoor-transit (subways and train cars) environments. This study identifies to the role of the three-dimensional urban landscape in structuring atmospheric microbiomes and employs mobile monitoring over ~1920 kilometers to measure continuous biodiversity. We found more diverse communities outdoors that significantly differ from indoor-transit air in microbial community structure, function, likely source environment, and potentially pathogenic fraction of the community. Variation in the structure of the urban landscape was associated with diversity and function of the near-surface atmospheric microbiome in outdoor samples.ImportanceGlobal nutrient cycling and human health depend on the rich biodiversity of microorganisms. The influence of the urban environment on microbiomes remains poorly described despite cities being the fastest growing ecosystems. All life is exposed to the atmosphere and thus discerning what microbes are present and what their functions may be is critical to create resistant and resilient cities under climate change. This study combines a spatially explicit analysis of urban structure with mobile monitoring of the atmospheric microbiome.

ACS Style

Jd Stewart; P. Kremer; Kabindra Shakya; M. Conway; A. Saad. Outdoor Atmospheric Microbial Diversity is Associated with Urban Landscape Structure and Differs from Indoor-Transit Systems as Revealed by Mobile Monitoring and Three-Dimensional Spatial Analysis. 2020, 1 .

AMA Style

Jd Stewart, P. Kremer, Kabindra Shakya, M. Conway, A. Saad. Outdoor Atmospheric Microbial Diversity is Associated with Urban Landscape Structure and Differs from Indoor-Transit Systems as Revealed by Mobile Monitoring and Three-Dimensional Spatial Analysis. . 2020; ():1.

Chicago/Turabian Style

Jd Stewart; P. Kremer; Kabindra Shakya; M. Conway; A. Saad. 2020. "Outdoor Atmospheric Microbial Diversity is Associated with Urban Landscape Structure and Differs from Indoor-Transit Systems as Revealed by Mobile Monitoring and Three-Dimensional Spatial Analysis." , no. : 1.

Journal article
Published: 01 April 2020 in Science of The Total Environment
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Microorganisms are abundant in the near surface atmosphere and make up a significant fraction of organic aerosols with implications on both human health and ecosystem services. Despite their importance, studies investigating biogeographical patterns of the atmospheric microbiome between urban and suburban areas are limited. Urban and suburban locations (including their microbial communities) vary considerably depending on climate, topography, industrial activities, demographics and other socio-economic factors. Hence, we need more location-specific data to make informed decision affecting air quality, human health, and the implication of a changing climate and policy decisions. The objective of this study was to describe how the atmospheric microbiome varies in composition and function between urban and suburban sites. We used high-throughput sequencing to analyze microbial communities collected at different times from PM2.5 samples collected by active sampling method (using a pump and an impactor) and dust settling of TSP collected by passive sampling method (no pump and no impactor) from an urban and suburban site. We found diverse communities unique in composition at both sites with equivalent functional potential. Taxonomic composition varied significantly with Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes, and Other phyla in greater relative abundance at the urban site. In contrast, Cyanobacteria, Tenericutes, Fusobacteria, and Deinococcus, were enriched at the suburban site. Community diversity also demonstrated a high degree of temporal variation within site. We identified over one-third of the communities as potentially pathogenic taxa (urban: 47.52% ± 14.40%, suburban: 34.53% ± 14.60%) and determined the majority of organisms come from animal-associated host or are environmental non-specific. Potentially pathogenic taxa and source environments were similar between active- and passive- sampling method results. Our research is novel it adds to the underrepresented set of studies on atmospheric microbial structure and function across land types and is the first to compare suburban and urban atmospheric communities.

ACS Style

Justin Stewart; K.M. Shakya; T. Bilinski; J.W. Wilson; S. Ravi; Chong Seok Choi. Variation of near surface atmosphere microbial communities at an urban and a suburban site in Philadelphia, PA, USA. Science of The Total Environment 2020, 724, 138353 .

AMA Style

Justin Stewart, K.M. Shakya, T. Bilinski, J.W. Wilson, S. Ravi, Chong Seok Choi. Variation of near surface atmosphere microbial communities at an urban and a suburban site in Philadelphia, PA, USA. Science of The Total Environment. 2020; 724 ():138353.

Chicago/Turabian Style

Justin Stewart; K.M. Shakya; T. Bilinski; J.W. Wilson; S. Ravi; Chong Seok Choi. 2020. "Variation of near surface atmosphere microbial communities at an urban and a suburban site in Philadelphia, PA, USA." Science of The Total Environment 724, no. : 138353.

Journal article
Published: 11 September 2019 in Environmental Pollution
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Mobile monitoring is a useful approach for measuring intra-urban variation of air pollution in urban environments. In this study, we used a mobile monitoring approach to study the spatial-temporal variability of air and noise pollution in urban neighborhoods of Philadelphia. During summer 2017, we used portable instruments to measure PM2.5, black carbon (BC), and noise levels along 5 km paths in four residential neighborhoods (Tioga, Mill Creek, Chestnut Hill, and Northern Liberties) and one commercial district (Center City) in Philadelphia, Pennsylvania, USA. A total of 62 sets of measurements were made at three different times of day (during morning rush hour, mid-afternoon, and during afternoon rush hour) from June 5 to July 7, 2017. Spatially, there was a significant difference in PM2.5 concentrations among the four residential neighborhoods. Overall, the Chestnut Hill neighborhood had the highest PM2.5 concentrations (13.25 ± 6.89 μg/m3), followed by Tioga (9.58 ± 4.83 μg/m3), Northern Liberties (7.02 ± 4.17 μg/m3), and Mill Creek (3.9 ± 4.5 μg/m3). There was temporal variability of pollutants depending on the neighborhood; Northern Liberties demonstrated the highest temporal variability in these data. The highest PM2.5 (18.86 ± 3.17 mg/m3) was measured in the Chestnut Hill neighborhood during mid-afternoon. Mean PM2.5, BC, and noise levels based on mobile measurements at Philadelphia during summer 2017 were 8.41 ± 4.31 μg/m3, 0.99 ± 0.44 μg C/m3, and 62.01 ± 3.20 dBA, respectively. Environmental noise showed the highest temporal variation of the monitored components for 3 time periods. In general, tree cover showed a weak and inconclusive association with particulate pollution levels.

ACS Style

Kabindra M. Shakya; Peleg Kremer; Kate Henderson; Meghan McMahon; Richard E. Peltier; Samantha Bromberg; Justin Stewart. Mobile monitoring of air and noise pollution in Philadelphia neighborhoods during summer 2017. Environmental Pollution 2019, 255, 113195 .

AMA Style

Kabindra M. Shakya, Peleg Kremer, Kate Henderson, Meghan McMahon, Richard E. Peltier, Samantha Bromberg, Justin Stewart. Mobile monitoring of air and noise pollution in Philadelphia neighborhoods during summer 2017. Environmental Pollution. 2019; 255 ():113195.

Chicago/Turabian Style

Kabindra M. Shakya; Peleg Kremer; Kate Henderson; Meghan McMahon; Richard E. Peltier; Samantha Bromberg; Justin Stewart. 2019. "Mobile monitoring of air and noise pollution in Philadelphia neighborhoods during summer 2017." Environmental Pollution 255, no. : 113195.

Journal article
Published: 29 January 2019 in International Journal of Environmental Research and Public Health
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Air pollution is a major environmental problem in the Kathmandu Valley. Specifically, roadside and traffic-related air pollution exposure levels were found at very high levels exceeding Nepal air quality standards for daily PM2.5. In an exposure study involving traffic police officers, we collected 78 blood samples in a highly polluted spring season (16 February 2014–4 April 2014) and 63 blood samples in the less polluted summer season (20 July 2014–22 August 2014). Fourteen biomarkers, i.e., C-reactive protein (CRP), serum amyloid A (SAA), intracellular adhesion molecule (ICAM-1), vascular cell adhesion molecule (VCAM-1), interferon gamma (IFN-γ), interleukins (IL1-β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IL-13), and tumor necrosis factor (TNF-α) were analyzed in collected blood samples using proinflammatory panel 1 kits and vascular injury panel 2 kits. All the inflammatory biomarker levels were higher in the summer season than in the spring season, while particulate levels were higher in the spring season than in the summer season. We did not find significant association between 24-hour average PM2.5 or black carbon (BC) exposure levels with most of analyzed biomarkers for the traffic volunteers working and residing near busy roads in Kathmandu, Nepal, during 2014. Inflammation and vascular injury marker concentrations were generally higher in females, suggesting the important role of gender in inflammation biomarkers. Because of the small sample size of female subjects, further investigation with a larger sample size is required to confirm the role of gender in inflammation biomarkers.

ACS Style

Kabindra M. Shakya; Richard E. Peltier; Yimin Zhang; Basu D. Pandey. Roadside Exposure and Inflammation Biomarkers among a Cohort of Traffic Police in Kathmandu, Nepal. International Journal of Environmental Research and Public Health 2019, 16, 377 .

AMA Style

Kabindra M. Shakya, Richard E. Peltier, Yimin Zhang, Basu D. Pandey. Roadside Exposure and Inflammation Biomarkers among a Cohort of Traffic Police in Kathmandu, Nepal. International Journal of Environmental Research and Public Health. 2019; 16 (3):377.

Chicago/Turabian Style

Kabindra M. Shakya; Richard E. Peltier; Yimin Zhang; Basu D. Pandey. 2019. "Roadside Exposure and Inflammation Biomarkers among a Cohort of Traffic Police in Kathmandu, Nepal." International Journal of Environmental Research and Public Health 16, no. 3: 377.