Estimated Impacts of Prescribed Fires on Air Quality and Premature Deaths in Georgia and Surrounding Areas in the US, 2015-2020.

Smoke from wildfires poses a substantial threat to health in communities near and far. To mitigate the extent and potential damage of wildfires, prescribed burning techniques are commonly employed as land management tools; however, they introduce their own smoke-related risks. This study investigates the impact of prescribed fires on daily average PM2.5 and maximum daily 8-h averaged O3 (MDA8-O3) concentrations and estimates premature deaths associated with short-term exposure to prescribed fire PM2.5 and MDA8-O3 in Georgia and surrounding areas of the Southeastern US from 2015 to 2020. Our findings indicate that over the study domain, prescribed fire contributes to average daily PM2.5 by 0.94 ± 1.45 µg/m3 (mean ± standard deviation), accounting for 14.0% of year-round ambient PM2.5. Higher average daily contributions were predicted during the extensive burning season (January-April): 1.43 ± 1.97 µg/m3 (20.0% of ambient PM2.5). Additionally, prescribed burning is also responsible for an annual average increase of 0.36 ± 0.61 ppb in MDA8-O3 (approximately 0.8% of ambient MDA8-O3) and 1.3% (0.62 ± 0.88 ppb) during the extensive burning season. We estimate that short-term exposure to prescribed fire PM2.5 and MDA8-O3 could have caused 2665 (95% confidence interval (CI): 2249-3080) and 233 (95% CI: 148-317) excess deaths, respectively. These results suggest that smoke from prescribed burns increases the mortality. However, refraining from such burns may escalate the risk of wildfires; therefore, the trade-offs between the health impacts of wildfires and prescribed fires, including morbidity, need to be taken into consideration in future studies.

Nomogram for predicting risk factors of fever in patients with negative preoperative urine culture after retrograde intrarenal surgery.

PURPOSE: To determine the risk factors for postoperative fever after retrograde intrarenal surgery (RIRS) in patients with negative preoperative urine culture (UC), and to establish a nomogram for predicting postoperative fever based on these risk factors. METHODS: This study collected 322 patients with negative UC who received RIRS at the First Affiliated Hospital of Anhui Medical University from March 2019 to May 2022. The study population was divided into a fever group and a non-fever group. The risk factors of postoperative fever were determined by univariate and multivariate logistic regression analyses, and a nomogram was established. The nomogram was evaluated in terms of differentiation, calibration, and clinical practicability. RESULTS: In this study, 47 (14.6%) patients developed a fever after surgery. Multivariate logistic regression analysis showed that for patients with negative preoperative urine culture, urinary leucocyte esterase (P = 0.005), operative time (P = 0.019), and intraoperative hypotension (P = 0.028) were independent risk factors of postoperative fever, and a nomogram was constructed according to the above variables. The area under the curve (AUC) calculated by receiver operating characteristic (ROC) analysis was 0.807 (95% CI 0.739-0.876), indicating good discrimination. The calibration curves showed good consistency, and the clinical decision curve analysis (DCA) showed the clinical applicability of the model. CONCLUSIONS: For patients with negative preoperative urine culture, urine leukocyte esterase, operative time, and intraoperative hypotension are independent risk factors of postoperative fever. The new nomogram can better assess the risk of infection in patients with negative UC after RIRS.

Change of Composition, Source Contribution, and Oxidative Effects of Environmental PM2.5 in the Respiratory Tract.

Fine particulate matter is a leading air pollutant, and its composition profile relates to sources and health effects. The human respiratory tract hosts a warmer and more humid microenvironment in contrast with peripheral environments. However, how the human respiratory tract impacts the transformation of the composition of environmental PM2.5 once they are inhaled and consequently changes of source contribution and health effects are unknown. Here, we show that the respiratory tract can make these properties of PM2.5 reaching the lung different from environmental PM2.5. We found via an in vitro model that the warm and humid conditions drive the desorption of nitrate (about 60%) and ammonium (about 31%) out of PM2.5 during the inhalation process and consequently make source contribution profiles for respiratory tract-deposited PM2.5 different from that for environmental PM2.5 as suggested in 11 Chinese cities and 12 US cities. We also observed that oxidative potential, one of the main health risk causes of PM2.5, increases by 41% after PM2.5 travels through the respiratory tract model. Our results reveal that PM2.5 inhaled in the lung differs from environmental PM2.5. This work provides a starting point for more health-oriented source apportionment, physiology-based health evaluation, and cost-effective control of PM2.5 pollution.

Feasibility and safety of bipolar-plasmakinetic transurethral enucleation and resection of the prostate in day surgery mode. / åŒæžç­‰ç¦»å­ç»å°¿é“å‰åˆ—è ºå‰œåˆ‡æ—¥é—´æ‰‹æœ¯ä¸´åºŠå®žè·µ.

OBJECTIVES: To evaluate the feasibility and safety of bipolar-plasmakinetic transurethral enucleation and resection of the prostate (B-TUERP) in day surgery. METHODS: From January 2021 to August 2022, 34 patients with benign prostatic hyperplasia (BPH) underwent B-TUERP in day surgery in the First Affiliated Hospital of Anhui Medical University. Patients completed the screening and anesthesia evaluation before admission and received the standard surgery which implements "anatomical enucleation of the prostate" and "absolute bleeding control" on the same day of admission, and by the same doctor. Bladder irrigation was stopped, catheter was removed and the discharge evaluation was performed on the first day after operation. The baseline data, perioperative conditions, time of recovery, treatment outcomes, hospitalization costs, and postoperative complications were analyzed. RESULTS: All operations were successfully conducted. The average age of the patients was (62.2±7.8) years, average prostate volume was (50.2±29.3) mL. The average operation time was (36.5±19.1) min, the average hemoglobin and blood sodium were decreased by (16.2±7.1) g/L and (2.2±2.0) mmol/L, respectively. The average postoperative length of hospital stay, and total length of hospital stay were (17.7±2.2) and (20.8±2.1) h, respectively, and the average hospitalization cost was (13 558±2320) CNY. All patients were discharged on the day after surgery except for one patient who was transferred to a general ward. Three patients received indwelling catheterization after catheter removal. The 3-month follow-up results showed a substantial improvement in the International Prostate Symptom Score, quality of life score and maximum urinary flow rate (all P<0.01). Three patients experienced temporary urinary incontinence, 1 patient experienced urinary tract infection, 4 patients were diagnosed with urethral stricture and 2 patients experienced bladder neck contracture. No complications above Clavien grade Ⅱ occurred. CONCLUSIONS: The preliminary results showed that B-TUERP ambulatory surgery is a safe, feasible, economical and effective treatment for appropriately selected patients with BPH.

Developing air pollution concentration fields for health studies using multiple methods: Cross-comparison and evaluation.

Past air pollution epidemiological studies have used a wide range of methods to develop concentration fields for health analyses. The fields developed differ considerably among these methods. The reasons for these differences and comparisons of their strengths, as well as the limitations for estimating exposures, remains under-investigated. Here, we applied nine methods to develop fields of eight pollutants (carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), fine particulate matter (PM2.5), and three speciated PM2.5 constituents including elemental carbon (EC), organic carbon (OC), and sulfate (SO4)) for the metropolitan Atlanta region for five years. The nine methods are Central Monitor (CM), Site Average (SA), Inverse Distance Weighting (IDW), Kriging (KRIG), Land Use Regression (LUR), satellite Aerosol Optical Depth (AOD), CMAQ model, CMAQ with kriging adjustment (CMAQ-KRIG), and CMAQ based data fusion (CMAQ-DF). Additionally, we applied an increasingly popular method, Random Forest (RF), and compared its results for NO2 and PM2.5 with other methods. For statistical evaluation, we focused our discussion on the temporal coefficient of determination, although other metrics are also calculated. Raw output from the CMAQ model contains modeling biases and errors, which are partially mitigated by fusing observational data in the CMAQ-KRIG and CMAQ-DF methods. Based on analyses that simulated model responses to more limited input data, the RF model is more robust and outperforms LUR for PM2.5. These results suggest RF may have potential in air pollution health studies, especially when limited measurement data are available. The RF method has several important weaknesses, including a relatively poor performance for NO2, diagnostic challenges, and computational intensiveness. The results of this study will help to improve our understanding of the strengths and weaknesses of different methods for estimating air pollutant exposures in epidemiological studies.

Novel Method for Ozone Isopleth Construction and Diagnosis for the Ozone Control Strategy of Chinese Cities.

Ozone (O3) isopleths describe the nonlinear responses of O3 concentrations to changes in nitrogen oxides (NOX) and volatile organic compounds (VOCs) and thus are pivotal to the determination of O3 control requirements. In this study, we innovatively use the Community Multiscale Air Quality model with the high-order decoupled direct method (CMAQ-HDDM) to simulate O3 pollution of China in 2017 and derive O3 isopleths for individual cities. Our simulation covering the entire China Mainland suggests severe O3 pollution as 97% of the residents experienced at least 1 day, in 2017, in excess of Chinese Level-II Ambient Air Quality Standards for O3 as 160 µg·m-3 (81.5 ppbV equally). The O3 responses to emissions of precursors vary widely across individual cities. Densely populated metropolitan areas such as Jing-Jin-Ji, Yangtze River Delta, and Pearl River Delta are following NOX-saturated regimes, where a small amount of NOX reduction increases O3. Ambient O3 pollution in the eastern region generally is limited by VOCs, while in the west by NOX. The city-specific O3 isopleths generated in this study are instrumental in forming hybrid and differentiated strategies for O3 abatement in China.

Estimating US Background Ozone Using Data Fusion.

US background (US-B) ozone (O3) is the O3 that would be present in the absence of US anthropogenic (US-A) emissions. US-B O3 varies by location and season and can make up a large, sometimes dominant, portion of total O3. Typically, US-B O3 is quantified using a chemical transport model (CTM) though results are uncertain due to potential errors in model process descriptions and inputs, and there are significant differences in various model estimates of US-B O3. We develop and apply a method to fuse observed O3 with US-B O3 simulated by a regional CTM (CMAQ). We apportion the model bias as a function of space and time to US-B and US-A O3. Trends in O3 bias are explored across different simulation years and varying model scales. We found that the CTM US-B O3 estimate was typically biased low in spring and high in fall across years (2016-2017) and model scales. US-A O3 was biased high on average, with bias increasing for coarser resolution simulations. With the application of our data fusion bias adjustment method, we estimate a 28% improvement in the agreement of adjusted US-B O3. Across the four estimates, we found annual mean CTM-simulated US-B O3 ranging from 30 to 37 ppb with the spring mean ranging from 32 to 39 ppb. After applying the bias adjustment, we found annual mean US-B O3 ranging from 32 to 33 ppb with the spring mean ranging from 37 to 39 ppb.

Statin and the risk of hepatocellular carcinoma in patients with hepatitis B virus or hepatitis C virus infection: a meta-analysis.

BACKGROUND: Statin may confer anticancer effect. However, the association between statin and risk of hepatocellular carcinoma (HCC) in patients with hepatitis B virus (HBV) or hepatitis C (HCV) virus infection remains inconsistent according to results of previous studies. A meta-analysis was performed to summarize current evidence. METHODS: Related follow-up studies were obtained by systematic search of PubMed, Cochrane's Library, and Embase databases. A random-effect model was used to for the meta-analysis. Stratified analyses were performed to evaluate the influences of study characteristics on the outcome. RESULTS: Thirteen studies with 519,707 patients were included. Statin use was associated with reduced risk of HCC in these patients (risk ratio [RR]: 0.54, 95% CI: 0.44 to 0.66, p < 0.001; I2 = 86%). Stratified analyses showed that the association between statin use and reduced HCC risk was consistent in patients with HBV or HCV infection, in elder (≥ 50 years) or younger (< 50 years) patients, in males or females, in diabetic or non-diabetic, and in those with or without cirrhosis (all p < 0.05). Moreover, lipophilic statins was associated with a reduced HCC risk (RR: 0.52, p < 0.001), but not for hydrophilic statins (RR: 0.89, p = 0.21). The association was more remarkable in patients with highest statin accumulative dose compared to those with lowest accumulative dose (p = 0.002). CONCLUSIONS: Satin use was independently associated with a reduced risk of HCC in patients with HBV or HCV infection.

Drought Impacts on Secondary Organic Aerosol: A Case Study in the Southeast United States.

Secondary organic aerosol (SOA) is a significant component of fine particulate matter, and it has increased during past drought periods in the U.S. Here, we use the Community Multiscale Air Quality (CMAQ) model to characterize the complex effects of drought on SOA through a case study comparing a drought period (June 2011) and a wet period (June 2013) over the southeast U.S. The model simulates a 68% (1.7 µg/m3) higher SOA concentration at the surface during drought and attributes 98% of this increase to biogenic SOA. Through model sensitivity simulations, the SOA increase associated with drought is attributed to 54% from accelerated gas-phase reactions oxidizing volatile organic compounds (VOCs) to SOA, 45% from higher emissions of biogenic VOCs, 18% from enhanced acid-catalyzed production of isoprene SOA in aerosol water due to changing sulfate, 3% from enhanced in-cloud aqueous phase chemistry. Because the higher SOA levels overwhelm the reduced precipitation, there is an increase in wet deposition flux in the drought month which offsets 20% of the total SOA increase. If anthropogenic emissions are held constant, anthropogenic SOA is 51% higher during drought, highlighting the importance of meteorological impacts on chemistry.

Fusion Method Combining Ground-Level Observations with Chemical Transport Model Predictions Using an Ensemble Deep Learning Framework: Application in China to Estimate Spatiotemporally-Resolved PM2.5 Exposure Fields in 2014-2017.

Atmospheric chemical transport models (CTMs) have been widely used to simulate spatiotemporally resolved PM2.5 concentrations. However, CTM results are usually prone to bias and errors. In this study, we improved the accuracy of PM2.5 predictions by developing an ensemble deep learning framework to fuse model simulations with ground-level observations. The framework encompasses four machine-learning models, i.e., general linear model, fully connected neural network, random forest, and gradient boosting machine, and combines them by stacking approach. This framework is applied to PM2.5 concentrations simulated by the Community Multiscale Air Quality (CMAQ) model for China from 2014 to 2017, which has complete spatial coverage over the entirety of China at a 12-km resolution, with no sampling biases. The fused PM2.5 concentration fields were evaluated by comparing with an independent network of observations. The R2 values increased from 0.39 to 0.64, and the RMSE values decreased from 33.7 µg/m3 to 24.8 µg/m3. According to the fused data, the percentage of Chinese population residing under the level II National Ambient Air Quality Standards of 35 µg/m3 for PM2.5 has increased from 46.5% in 2014 to 61.7% in 2017. The method is readily adapted to utilize near-real-time observations for operational analyses and forecasting of pollutant concentrations and can be extended to provide source apportionment forecasts as well.

Fault Identification of Chemical Processes Based on k-NN Variable Contribution and CNN Data Reconstruction Methods.

Data-driven fault detection and identification methods are important in large-scale chemical processes. However, some traditional methods often fail to show superior performance owing to the self-limitations and the characteristics of process data, such as nonlinearity, non-Gaussian distribution, and multi-operating mode. To cope with these issues, the k-NN (k-Nearest Neighbor) fault detection method and extensions have been developed in recent years. Nevertheless, these methods are primarily used for fault detection, and few papers can be found that examine fault identification. In this paper, in order to extract effective fault information, the relationship between various faults and abnormal variables is studied, and an accurate "fault⁻symptom" table is presented. Then, a novel fault identification method based on k-NN variable contribution and CNN data reconstruction theories is proposed. When there is an abnormality, a variable contribution plot method based on k-NN is used to calculate the contribution index of each variable, and the feasibility of this method is verified by contribution decomposition theory, which includes a feasibility analysis of a single abnormal variable and multiple abnormal variables. Furthermore, to identify all the faulty variables, a CNN (Center-based Nearest Neighbor) data reconstruction method is proposed; the variables that have the larger contribution indices can be reconstructed using the CNN reconstruction method in turn. The proposed search strategy can guarantee that all faulty variables are found in each sample. The reliability and validity of the proposed method are verified by a numerical example and the Continuous Stirred Tank Reactor system.

Development of PM2.5 Source Profiles Using a Hybrid Chemical Transport-Receptor Modeling Approach.

Laboratory-based or in situ PM2.5 source profiles may not represent the pollutant composition for the sources in a different study location due to spatially and temporally varying characteristics, such as fuel or crustal element composition, or due to differences in emissions behavior under ambient versus laboratory conditions. In this work, PM2.5 source profiles were estimated for 20 sources using a novel optimization approach that incorporates observed concentrations with source impacts from a chemical transport model (CTM) to capture local pollutant characteristics. Nonlinear optimization was used to minimize the error between source profiles, CTM source impacts, and observations. In a 2006 U.S. application, spatial and seasonal variability was seen for coal combustion, dust, fires, metals processing, and other source profiles when compared to the reference profiles, with variability in species fractions over 400% (calcium in dust) compared to mean contributions of the same species. Revised profiles improved the spatial and temporal bias in modeled concentrations of several trace metal species, including Na, Al, Ca, Mn, Cu, As, Se, Br, and Pb. In an application of the CMB-iteration model for two U.S. cities, revised profiles estimated higher biomass burning and dust impacts for summer compared with previous studies. Source profile optimization can be useful for source apportionment studies that have limited availability of source profile data for the location of interest.

Responses in Ozone and Its Production Efficiency Attributable to Recent and Future Emissions Changes in the Eastern United States.

Ozone production efficiency (OPE), a measure of the number of ozone (O3) molecules produced per emitted NOX (NO + NO2) molecule, helps establish the relationship between NOX emissions and O3 formation. We estimate long-term OPE variability across the eastern United States using two novel approaches: an observation-based empirical method and a chemical transport model (CTM) method. The CTM approach explicitly controls for differing O3 and NOX reaction product (NOZ) deposition rates and separately estimates OPEs from on-road mobile and electricity generating unit sources across a broad spatial scale. We find lower OPEs in urban areas and that average July OPE increased over the eastern United States domain between 2001 and 2011 from 11 to 14. CTM and empirical approaches agree at low NOZ concentrations, but CTM OPEs are greater than empirical OPEs at high NOZ. Our results support that NOX emissions reductions become more effective at reducing O3 at lower NOZ concentrations. Electricity generating unit OPEs are higher than mobile OPEs except near emissions locations, meaning further utility NOX emissions reductions will have greater per unit impacts on O3 regionally.

A New Combined Stepwise-Based High-Order Decoupled Direct and Reduced-Form Method To Improve Uncertainty Analysis in PM2.5 Simulations.

The traditional reduced-form model (RFM) based on the high-order decoupled direct method (HDDM), is an efficient uncertainty analysis approach for air quality models, but it has large biases in uncertainty propagation due to the limitation of the HDDM in predicting nonlinear responses to large perturbations of model inputs. To overcome the limitation, a new stepwise-based RFM method that combines several sets of local sensitive coefficients under different conditions is proposed. Evaluations reveal that the new RFM improves the prediction of nonlinear responses. The new method is applied to quantify uncertainties in simulated PM2.5 concentrations in the Pearl River Delta (PRD) region of China as a case study. Results show that the average uncertainty range of hourly PM2.5 concentrations is -28% to 57%, which can cover approximately 70% of the observed PM2.5 concentrations, while the traditional RFM underestimates the upper bound of the uncertainty range by 1-6%. Using a variance-based method, the PM2.5 boundary conditions and primary PM2.5 emissions are found to be the two major uncertainty sources in PM2.5 simulations. The new RFM better quantifies the uncertainty range in model simulations and can be applied to improve applications that rely on uncertainty information.

Improving the Accuracy of Daily PM2.5 Distributions Derived from the Fusion of Ground-Level Measurements with Aerosol Optical Depth Observations, a Case Study in North China.

The accuracy in estimated fine particulate matter concentrations (PM2.5), obtained by fusing of station-based measurements and satellite-based aerosol optical depth (AOD), is often reduced without accounting for the spatial and temporal variations in PM2.5 and missing AOD observations. In this study, a city-specific linear regression model was first developed to fill in missing AOD data. A novel interpolation-based variable, PM2.5 spatial interpolator (PMSI2.5), was also introduced to account for the spatial dependence in PM2.5 across grid cells. A Bayesian hierarchical model was then developed to estimate spatiotemporal relationships between AOD and PM2.5. These methods were evaluated through a city-specific 10-fold cross-validation procedure in a case study in North China in 2014. The cross validation R(2) was 0.61 when PMSI2.5 was included and 0.48 when PMSI2.5 was excluded. The gap-filled AOD values also effectively improved predicted PM2.5 concentrations with an R(2) = 0.78. Daily ground-level PM2.5 concentration fields at a 12 km resolution were predicted with complete spatial and temporal coverage. This study also indicates that model prediction performance should be assessed by accounting for monitor clustering due to the potential misinterpretation of model accuracy in spatial prediction when validation monitors are randomly selected.

Impacts of potential CO2-reduction policies on air quality in the United States.

Impacts of emissions changes from four potential U.S. CO2 emission reduction policies on 2050 air quality are analyzed using the community multiscale air quality model (CMAQ). Future meteorology was downscaled from the Goddard Institute for Space Studies (GISS) ModelE General Circulation Model (GCM) to the regional scale using the Weather Research Forecasting (WRF) model. We use emissions growth factors from the EPAUS9r MARKAL model to project emissions inventories for two climate tax scenarios, a combined transportation and energy scenario, a biomass energy scenario and a reference case. Implementation of a relatively aggressive carbon tax leads to improved PM2.5 air quality compared to the reference case as incentives increase for facilities to install flue-gas desulfurization (FGD) and carbon capture and sequestration (CCS) technologies. However, less capital is available to install NOX reduction technologies, resulting in an O3 increase. A policy aimed at reducing CO2 from the transportation sector and electricity production sectors leads to reduced emissions of mobile source NOX, thus reducing O3. Over most of the U.S., this scenario leads to reduced PM2.5 concentrations. However, increased primary PM2.5 emissions associated with fuel switching in the residential and industrial sectors leads to increased organic matter (OM) and PM2.5 in some cities.

The Gradient Effect on Cyclic Behavior of 316L Stainless Steel in the Ultrasonic Bending Test.

Nanoindentation measurements were conducted to investigate the high-cycle response of 316L stainless steel in bending fatigue. Hardness variation owing to the gradient flexure stress amplitude for different curvatures was plotted along with the thickness and length, respectively. Scanning electron microscopy (SEM) was subsequently conducted to explore the deformation characteristics in multiple layers, which had cyclic gradient stress, on the cross-section of specimens. The nanoindentation results indicated that the cyclic hardening response of 316L stainless steel is correlated with the level of stress amplitude in the high-cycle fatigue (HCF) regime. Furthermore, an analytical model was proposed to clarify the relationship between nanohardness and stress amplitude. Finally, the evolution of damage accumulation due to irreversible plastic deformation is continuous during stress reduction up to the neighboring zone at the neutral surface of the flexure beam in some individual grains.

Impact of the 2022 New Mexico, US wildfires on air quality and health.

The 2022 wildfires in New Mexico, United States, were unparalleled compared to past wildfires in the state in both their scale and intensity, resulting in poor air quality and a catastrophic loss of habitat and livelihood. Among all wildfires in New Mexico in 2022, six wildfires were selected for our study based on the size of the burn area and their proximity to populated areas. These fires accounted for approximately 90 % of the total burn area in New Mexico in 2022. We used a regional chemical transport model and data-fusion technique to quantify the contribution of these six wildfires (April 6 to August 22) on particulate matter (PM2.5: diameter ≤ 2.5 µm) and ozone (O3) concentrations, as well as the associated health impacts from short-term exposure. We estimated that these six wildfires emitted 152 thousand tons of PM2.5 and 287 thousand tons of volatile organic compounds to the atmosphere. We estimated that the average daily wildfire smoke PM2.5 across New Mexico was 0.3 µg/m3, though 1 h maximum exceeded 120 µg/m3 near Santa Fe. Average wildfire smoke maximum daily average 8-h O3 (MDA8-O3) contribution was 0.2 ppb during the study period over New Mexico. However, over the state 1 h maximum smoke O3 exceeded 60 ppb in some locations near Santa Fe. Estimated all-cause excess mortality attributable to short term exposure to wildfire PM2.5 and MDA8-O3 from these six wildfires were 18 (95 % Confidence Interval (CI), 15-21) and 4 (95 % CI: 3-6) deaths. Additionally, we estimate that wildfire PM2.5 was responsible for 171 (95 %: 124-217) excess cases of asthma emergency department visits. Our findings underscore the impact of wildfires on air quality and human health risks, which are anticipated to intensify with global warming, even as local anthropogenic emissions decline.

Climate change adaptation through urban heat management in Atlanta, Georgia.

This study explores the potential effectiveness of metropolitan land cover change as a climate change adaptation strategy for managing rising temperatures in a large and rapidly warming metropolitan region of the United States. Through the integration of a mesoscale meteorological model with estimated land cover data for the Atlanta, Georgia region in 2010, this study quantifies the influence of extensive land cover change at the periphery of a large metropolitan region on temperature within the city center. The first study to directly model a metropolitan scale heat transfer mechanism, we find both enhanced tree canopy and impervious cover in the suburban zones of the Atlanta region to produce statistically significant cooling and warming effects in the urban core. Based on these findings, we conclude that urban heat island management both within and beyond the central developed core of large cities may provide an effective climate change adaptation strategy for large metropolitan regions.