@article{raab_moyer_afrin_garcia-menendez_ward-caviness_2023, title={Prescribed fires, smoke exposure, and hospital utilization among heart failure patients}, volume={22}, ISSN={["1476-069X"]}, DOI={10.1186/s12940-023-01032-4}, abstractNote={Abstract
                Background
                Prescribed fires often have ecological benefits, but their environmental health risks have been infrequently studied. We investigated associations between residing near a prescribed fire, wildfire smoke exposure, and heart failure (HF) patients’ hospital utilization.
              
                Methods
                We used electronic health records from January 2014 to December 2016 in a North Carolina hospital-based cohort to determine HF diagnoses, primary residence, and hospital utilization. Using a cross-sectional study design, we associated the prescribed fire occurrences within 1, 2, and 5 km of the patients’ primary residence with the number of hospital visits and 7- and 30-day readmissions. To compare prescribed fire associations with those observed for wildfire smoke, we also associated zip code-level smoke density data designed to capture wildfire smoke emissions with hospital utilization amongst HF patients. Quasi-Poisson regression models were used for the number of hospital visits, while zero-inflated Poisson regression models were used for readmissions. All models were adjusted for age, sex, race, and neighborhood socioeconomic status and included an offset for follow-up time. The results are the percent change and the 95% confidence interval (CI).
              
                Results
                Associations between prescribed fire occurrences and hospital visits were generally null, with the few associations observed being with prescribed fires within 5 and 2 km of the primary residence in the negative direction but not the more restrictive 1 km radius. However, exposure to medium or heavy smoke (primarily from wildfires) at the zip code level was associated with both 7-day (8.5% increase; 95% CI = 1.5%, 16.0%) and 30-day readmissions (5.4%; 95% CI = 2.3%, 8.5%), and to a lesser degree, hospital visits (1.5%; 95% CI: 0.0%, 3.0%) matching previous studies.
              
                Conclusions
                Area-level smoke exposure driven by wildfires is positively associated with hospital utilization but not proximity to prescribed fires.
              }, number={1}, journal={ENVIRONMENTAL HEALTH}, author={Raab, Henry and Moyer, Joshua and Afrin, Sadia and Garcia-Menendez, Fernando and Ward-Caviness, Cavin K.}, year={2023}, month={Dec} }
 @article{huang_lal_qin_hu_russell_odman_afrin_garcia-menendez_susan m. o'neill_2021, title={Application and evaluation of a low-cost PM sensor and data fusion with CMAQ simulations to quantify the impacts of prescribed burning on air quality in Southwestern Georgia, USA}, volume={71}, ISSN={["2162-2906"]}, url={https://doi.org/10.1080/10962247.2021.1924311}, DOI={10.1080/10962247.2021.1924311}, abstractNote={ABSTRACT Prescribed burning (PB) is a prominent source of PM2.5 in the southeastern US and exposure to PB smoke is a health risk. As demand for burning increases and stricter controls are implemented for other anthropogenic sources, PB emissions tend to be responsible for an increasing fraction of PM2.5 concentrations. Here, to quantify the effect of PB on air quality, low-cost sensors are used to measure PM2.5 concentrations in Southwestern Georgia. The feasibility of using low-cost sensors as a supplemental measurement tool is evaluated by comparing them with reference instruments. A chemical transport model, CMAQ, is also used to simulate the contribution of PB to PM2.5 concentrations. Simulated PM2.5 concentrations are compared to observations from both low-cost sensors and reference monitors. Finally, a data fusion method is applied to generate hourly spatiotemporal exposure fields by fusing PM2.5 concentrations from the CMAQ model and all observations. The results show that the severe impact of PB on local air quality and public health may be missed due to the dearth of regulatory monitoring sites. In Southwestern Georgia PM2.5 concentrations are highly non-homogeneous and the spatial variation is not captured even with a 4-km horizontal resolution in model simulations. Low-cost PM sensors can improve the detection of PB impacts and provide useful spatial and temporal information for integration with air quality models. R2 of regression with observations increases from an average of 0.09 to 0.40 when data fusion is applied to model simulation withholding the observations at the evaluation site. Adding observations from low-cost sensors reduces the underestimation of nighttime PM2.5 concentrations and reproduces the peaks that are missed by the simulations. In the future, observations from a dense network of low-cost sensors could be fused with the model simulated PM2.5 fields to provide better estimates of hourly exposures to smoke from PB. Implications: Prescribed burning emissions are a major cause of elevated PM2.5 concentrations, posing a risk to human health. However, their impact cannot be quantified properly due to a dearth of regulatory monitoring sites in certain regions of the United States such as Southwestern Georgia. Low-cost PM sensors can be used as a supplemental measurement tool and provide useful spatial and temporal information for integration with air quality model simulations. In the future, data from a dense network of low-cost sensors could be fused with model simulated PM2.5 fields to provide improved estimates of hourly exposures to smoke from prescribed burning.}, number={7}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, publisher={Informa UK Limited}, author={Huang, Ran and Lal, Raj and Qin, Momei and Hu, Yongtao and Russell, Armistead G. and Odman, M. Talat and Afrin, Sadia and Garcia-Menendez, Fernando and Susan M. O'Neill}, year={2021}, month={Jul}, pages={815–829} }
 @article{afrin_garcia-menendez_2021, title={Potential impacts of prescribed fire smoke on public health and socially vulnerable populations in a Southeastern US state}, volume={794}, ISSN={["1879-1026"]}, url={http://dx.doi.org/10.1016/j.scitotenv.2021.148712}, DOI={10.1016/j.scitotenv.2021.148712}, abstractNote={Prescribed fire is an essential tool for wildfire risk mitigation and ecosystem restoration in the Southeastern United States. It is also one of the region's largest sources of atmospheric emissions. The public health impacts of prescribed fire smoke, however, remain uncertain. Here, we use digital burn permit records, reduced-complexity air quality modeling, and epidemiological associations between fine particulate matter concentrations and multiple health endpoints to assess the impacts of prescribed burning on public health across Georgia. Additionally, we examine the social vulnerability of populations near high prescribed burning activity using a demographic- and socioeconomic-based index. The analysis identifies spatial clusters of burning activity in the state and finds that areas with intense prescribed fire have levels of social vulnerability that are over 25% higher than the state average. The results also suggest that the impacts of burning in Georgia can potentially include hundreds of annual morbidity and mortality cases associated with smoke pollution. These health impacts are concentrated in areas with higher fractions of low socioeconomic status, elderly, and disabled residents, particularly vulnerable to air pollution. Estimated smoke-related health incidence rates are over 3 times larger than the state average in spatial clusters of intense burning activity, and over 40% larger in spatial clusters of high social vulnerability. Spatial clusters of low social vulnerability experience substantially lower negative health effects from prescribed burning relative to the rest of the state. The health burden of smoke from prescribed burns in the state is comparable to that estimated for other major emission sectors, such as vehicles and industrial combustion. Within spatial clusters of socially-vulnerable populations, the impacts of prescribed fire considerably outweigh those of other emission sectors. These findings call for greater attention to the air quality impacts of prescribed burning in the Southeastern U.S. and the communities most exposed to fire-related smoke.}, journal={SCIENCE OF THE TOTAL ENVIRONMENT}, publisher={Elsevier BV}, author={Afrin, Sadia and Garcia-Menendez, Fernando}, year={2021}, month={Nov} }
 @article{islam_afrin_tarek_rahman_2021, title={Reliability and financial feasibility assessment of a community rainwater harvesting system considering precipitation variability due to climate change}, volume={289}, ISSN={["1095-8630"]}, DOI={10.1016/j.jenvman.2021.112507}, abstractNote={This study proposes a community rainwater harvesting (RWH) system as an alternative water supply solution for Paikgacha, a water-scarce coastal urban area in Bangladesh. Although individual household-based RWH systems have been implemented in many areas in Bangladesh, to date, no study has been conducted designing a community RWH system and assessing its reliability and financial feasibility. This study employs historical observed and available climate model predicted future rainfall data into stormwater management model (SWMM) for rainfall-runoff simulation of the community RWH, and compares SWMM's performance with rational formula based estimation. We then calculate volumetric and time reliability of the proposed system and assess its financial viability. We observe good agreement in reliability curves generated by SWMM and rational formula-based model. Under the historical rainfall scenario, our proposed community RWH shows up to 99% reliability for 100 L per day household demand, given that proper community size and storage tank size are chosen. Predicted rainfall pattern of 2041–2070 period shows similar reliability-tank size relation to that of historical observed rainfall; however, predicted high precipitation intensity during 2021–2040 and 2071–2100 seem to assist the system in attaining higher reliability. Cost-benefit analysis indicates the financial viability of the proposed system. Finally, we develop a nomograph incorporating interactive factors of RWH, which would ease decision making by the policymakers regarding the implementation of community RWH.}, journal={JOURNAL OF ENVIRONMENTAL MANAGEMENT}, author={Islam, Mohammad Maksimul and Afrin, Sadia and Tarek, Mehedi Hasan and Rahman, Md Mujibur}, year={2021}, month={Jul} }
 @article{afrin_islam_ahmed_2021, title={A Meteorology Based Particulate Matter Prediction Model for Megacity Dhaka}, volume={21}, ISSN={["2071-1409"]}, DOI={10.4209/aaqr.2020.07.0371}, abstractNote={ABSTRACT 

Dhaka, the capital of Bangladesh, is one of the megacities in the world with the worst air quality. In this study, we develop statistical models for predicting particulate matter (PM) concentration in ambient air of Dhaka using meteorological and air quality data from 2002 to 2004 of a continuous air quality monitoring station (CAMS). Model for finer fraction of PM (PM2.5) explains up to 57% variability of daily PM2.5 concentration, whereas model for coarser fraction (PM2.5-10) explains up to 35% of its variability, indicating that PM2.5 is influenced more by meteorology than PM2.5-10. Temperature, wind speed, and wind direction account for 94% of total PM2.5 variability explained by the model, while relative humidity contributes to 75% of total PM2.5-10 variability. Inclusion of PM lag effect increases models’ predictive power by 4-16%. In general, our developed models show promising performance in capturing the seasonal variability of Dhaka’s PM concentration, although overestimate the low concentrations during wet season (April to September). We validate these models using a recent dataset (2013-2017) from the same monitoring site, in which modeled PM show strong positive correlations with observed concentrations (r = 0.81 and 0.76 for PM2.5 and PM2.5-10 respectively). Models also exhibit strong predictive power in forecasting PM levels of two other CAMSs in Dhaka. Thus, the developed models have potentials to explain the temporal and spatial variability of daily PM within Dhaka. These models can be helpful to policymakers as they can predict daily PM at any location of Dhaka with reasonable accuracy if daily meteorological data and previous day’s PM concentration are available. The effect of climate change scenarios on air pollution dynamics of Dhaka can also be assessed using these models.}, number={4}, journal={AEROSOL AND AIR QUALITY RESEARCH}, author={Afrin, Sadia and Islam, Mohammad Maksimul and Ahmed, Tanvir}, year={2021}, month={Apr} }