Effect of the Urban Heat Island on Aerosol pH - Environmental Science

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Effect of the Urban Heat Island on Aerosol pH Michael A Battaglia, Sarah Douglas, and Christopher J. Hennigan Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b02786 • Publication Date (Web): 19 Oct 2017 Downloaded from http://pubs.acs.org on October 22, 2017

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Environmental Science & Technology

Effect of the Urban Heat Island on Aerosol pH

Michael A. Battaglia Jr.1, Sarah Douglas1, Christopher J. Hennigan1*

1

Department of Chemical, Biochemical and Environmental Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250 *

To whom correspondence should be addressed: email [email protected]; phone: (410) 4553515

The urban heat island (UHI) is a widely observed phenomenon whereby urban environments have higher temperatures and different relative humidities than surrounding suburban and rural areas. Temperature (T) and relative humidity (RH) strongly affect the partitioning of semivolatile species found in the atmosphere, such as nitric acid, ammonia, and water. These species are inherently tied to aerosol pH, which is a key parameter driving some atmospheric chemical processes and environmental effects of aerosols. In this study, we characterized the effect of the UHI on aerosol pH in Baltimore, MD, and Chicago, IL. The T and RH differences that define the UHI lead to substantial differences in aerosol liquid water (ALW) content. The ALW differences produce urban aerosol pH that is systematically lower (more acidic) than rural aerosol pH for identical atmospheric composition. The UHI in Baltimore and Chicago are most intense during the summer and at night, with urban-rural aerosol pH differences in excess of 0.8 and 0.65 pH units, respectively. The UHI has been observed in cities of all sizes: the similarity of our results for cities with different climatologies and aerosol compositions suggests that these results have broad implications for chemistry occurring in and around urban atmospheres globally.

1. Introduction The urban heat island (UHI) is a meteorological phenomenon where anthropogenic modifications to the surface and atmosphere through urbanization lead to a local climate that is warmer than surrounding non-urbanized areas, especially noticeable at night.1 This anthropogenic urbanization increases the amount of absorbed solar radiation, decreases evapotranspiration through reduction of plant life and green space, increases runoff by use of impervious construction materials, augments surface friction through urban skylines, and causes

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the release of anthropogenic heat. The UHI is not limited to cities of specific latitude and longitude, and has been documented in cities on every continent except Antarctica.2, 3 The UHI has several important effects, both on the urban environment and the humans living within. Urbanization results in modification of regional near-surface air temperatures, horizontal wind patterns (and thus convective heat transfer), air quality, and precipitation patterns.4 Increasing temperature has been shown to increase ozone concentrations.5-7 In general, temperature and stagnant winds, both elements of the UHI, positively correlate with pollution levels.8 In some cities, precipitation frequency and intensity can be affected by the UHI,9, 10 with implications for atmospheric pollutant lifetimes. Additionally, the UHI effect can be enhanced by upstream urban environments, which may play a significant role in densely developed urban corridors.11 In aquatic environments, pH is considered a “master variable” since it affects so many chemical processes.12 The acidity (pH) of atmospheric particles plays a similarly critical role in a number of chemical and physical processes, and in their effects.13 Aerosol pH has been shown to control halogen activation and cycling in marine and coastal environments, with downstream effects on radical budgets, oxidation of volatile organic compounds (VOCs), and ozone formation.14 The oxidation of S(IV) to S(VI), with implications on the formation of sulfuric acid and sulfate aerosols, is also affected by aerosol pH.14 The resulting sulfate-rich, acidic aerosols are observed to play an important role in the dissolution of otherwise insoluble metal particles found in vehicular emissions, directly influencing the oxidative potential and toxicity of aerosols.15 The gas-particle phase partitioning of many semi-volatile species, such as ammonia/ammonium and nitric acid/nitrate, is strongly affected by aerosol pH.16, 17 Some inorganic salt constituents of PM2.5, such as ammonium sulfate ((NH4)2SO4) and ammonium


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bisulfate (NH4HSO4), not only contribute to the acidity of atmospheric aerosols through dissolution, but are hygroscopic in nature and impact the water content of particles.18 The radiative forcing influence of aerosols through light scattering and absorption (direct aerosol effect),19 and impacts on cloud lifetime and albedo (aerosol indirect effects)20, 21 are affected by particle hygroscopicity, and thus by particle acidity. In addition to the environmental and atmospheric chemistry effects of aerosol pH, the acidic component of PM has been associated with decreased lung function,22-24 increased hospital admission rates,25-27 and mortality.28 However, some studies have found no statistical significance for increased toxicity of the acidic PM fraction.29 Thus, the role of acidic PM in human health effects, or in specific subsets of the population24, 30 remains highly ambiguous. In addition to the direct health effects, particle acidity may also play a significant indirect role in human health effects through the acid-catalyzed pathways for the formation of secondary organic aerosol (SOA).31-34 SOA is a major component of fine PM,35 so acid-catalyzed reactions may increase the atmospheric PM burden and its associated health effects. Direct measurements of ambient aerosol pH do not currently exist. The two methods that are most accurate for predicting aerosol pH are gas-particle phase partitioning of semi-volatile species like ammonia or nitric acid,36 and thermodynamic equilibrium models run in the forward mode (gas + particle species concentration inputs).13 The pH of an aqueous solution is a measure of the activity of the hydrogen ion, and the IUPAC definition (https://goldbook.iupac.org/html/P/P04524.html) is:

pH = −log H = −logH ∙ γ H /

(1)

where a(H+) represents the hydrogen ion activity in aqueous solution, m(H+) represents the hydrogen ion molality, γ(H+) represents the molality-based activity coefficient, and mƟ = 1 mol



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kg-1 is the standard molality. While in equations and thermodynamic equilibrium models the hydrogen ion is often represented as H+ for simplicity, we note here that in aqueous systems this ion is not likely to exist; rather, it exists as species such as H3O+, and H2O5+. Additionally, the most common application of pH is based on the molar concentration of H+ ion. As most thermodynamic equilibrium models (ISORROPIA, E-AIM) report the species concentration per air volume (µg m-3 or µmol m-3), including H+ and H2O, the pH of an aerosol can be calculated by the following:37

pH

= −log γ H

= −log

!"#

$

(2)

where H+aq is the H+ concentration in solution in mol L-1, H+air is the H+ ion loading for an air sample in µg m-3, ALW is the aerosol liquid water content (µg m-3), and where the ISORROPIA-II model assumes γH+ is equal to unity.37 From Equation 2, it is apparent that there is an intrinsic link between aerosol pH and the water content associated with the particles. A prominent factor contributing to the UHI is a reduction in, or lack of, vegetation in dense urban environments. In combination with rapid rainwater runoff and limited soil moisture, this contributes to a significant decline in evaporation. The overall result is less moisture in the urban atmosphere and less fog and dew development in urban environments.38, 39 Further, higher temperatures in urban environments increase the vapor pressure of water. Taken together, urban atmospheres frequently have reduced absolute and relative humidity levels compared to surrounding suburban and rural areas. It is very likely that these conditions will result in lower aerosol liquid water content and a change in urban aerosol pH values versus the rural environment, even for identical atmospheric compositions. This effect has been explored in Chinese fogs,38 but never for aerosol chemical composition.


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To our knowledge, this is the first study to characterize the link between the UHI and aerosol pH. Our analysis is applied to Baltimore, MD, a location where the UHI phenomenon has been studied extensively,40-44 and to Chicago, IL, a location with different climatology and atmospheric chemical composition. The UHI follows distinct seasonal and diurnal patterns, so our analysis focuses on the diurnal behavior of aerosol pH during summer and winter.

2. Materials and Methods General Approach Our goal in this study is to quantify the effect of meteorological differences associated with the UHI on differences in ALW and aerosol pH. We employ the following two conditions in our analysis: 1) the assumption that aerosol chemical composition and gas-phase ammonia are constant throughout the day (although different in each location and season), and 2) the assumption that aerosol chemical composition and gas-phase ammonia concentrations are the same at the paired urban-rural sites. We recognize that there is error associated with these assumptions. However, the observational data that would be required to avoid such assumptions would be paired (urban-rural) measurements of aerosol chemical composition and gas-phase ammonia. These measurements would need to be highly time-resolved (hourly, or better) and extend over multiple seasons. We are not aware of any such data sets that meet these constraints. However, this apparent limitation actually offers us the opportunity to quantify differences in ALW and aerosol pH due solely to meteorological differences associated with the UHI. First, the winter and summer Baltimore and Chicago UHI was characterized using paired air temperature and relative humidity measurements from rural and urban meteorological stations. Aerosol composition data reflective of the two regions (Baltimore and Chicago) for winter and summer was compiled from speciated PM2.5 data collected at the rural site associated



with each location. The ISORROPIA-II aerosol thermodynamic equilibrium model45 was then used to investigate the effect of changing meteorological conditions associated with the urban heat island phenomenon on aerosol chemistry in Baltimore and Chicago. Inputs into the model consisted of aerosol inorganic chemical composition (Na+, SO42-, NH4+, NO3-, Cl-, Ca2+, K+, and Mg2+ ), gas-phase ammonia measurements, and paired hourly T and RH data collected at the rural and urban sites. A total of four model scenarios were performed to characterize the UHI and aerosol pH behavior in each location: rural-winter, urban-winter, rural-summer, and urbansummer (Supporting Information Table S1). Diurnal profiles of all relevant parameters were constructed from the collected meteorological data or model outputs, including the temperature and relative humidity differences between the urban and rural sites, aerosol liquid water content, and aerosol pH. The model inputs of atmospheric chemical composition and meteorological parameters were based upon multi-year observations to capture average conditions in each city. Within each season (summer or winter), the same aerosol chemical composition was used for the rural and urban simulations; however, the seasonal compositions were different for each city, and were calculated from observations made during those months. Thus, the inorganic aerosol composition (including gas + aerosol NH3) was identical for the urban-rural simulations within each season (Supporting Information Tables S2 and S3), an assumption that has been justified for the Baltimore area, and extended to the Chicago area.46 For each diurnal simulation performed, the chemical composition and concentration was assumed to be constant throughout the day. This was necessitated by the 24-h measurement time of the aerosol filter sampling. The long sample times, and assumption of constant composition throughout the day, while on the surface appears to be a statistically-limiting element of the current approach, it enables us to


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isolate and identify differences in aerosol chemistry due solely to meteorological differences between the urban and rural sites. In contrast to the constant aerosol composition, hourly meteorological data from paired urban-rural sites were used as inputs to the thermodynamic model. Site Descriptions For this study, the HU-Beltsville site was selected as the Baltimore rural site while a station in downtown Baltimore represents the urban site. The HU-Beltsville site (Howard University Beltsville Lab., 12003 Old Baltimore Pike, Beltsville, MD 20705; 39.055277°, 76.878333°) is described by Maryland Department of the Environment (MDE) as a suburban site with the nearest road source 385 m away, and is one of two sites in Maryland where speciated PM2.5 data is collected for regulatory purposes. At this site, PM2.5 chemical composition based upon 24-hour filter samples was measured every third day (https://www.epa.gov/outdoor-airquality-data). Meteorological data collected at the HU-Beltsville site was used for the rural data. Hourly measurements of barometric pressure, precipitation, dry bulb temperature, RH, solar radiation, UV radiation, and wind velocity are made at this site.47 Meteorological data in downtown Baltimore were collected from a National Oceanic and Atmospheric Administration (NOAA) monitoring station located in the Baltimore Inner Harbor (39.281°, -76.608°). The Inner Harbor NOAA station is located in a downtown park setting at the Maryland Science Center. It is located 34.2 km NE of the HU-Beltsville site. Hourly measurements of dry and wet bulb temperature, dew point, RH, wind velocity, barometric pressure, sky conditions, visibility, and precipitation are made at the site (https://www.ncdc.noaa.gov/cdo-web/datatools/lcd).



For Chicago, rural meteorological conditions were taken from the Romeoville Weather Forecast Office/Lewis University Airport NOAA met station (41.60413°, -88.08497°). The Lewis Airport met station is located on the operating runway of the airport at Lewis University, and near to the Romeoville weather office, 2.3 km NE of Prairie Bluff Preserve in a rural/suburban area. Meteorological data in downtown Chicago were collected from a NOAA monitoring station located at the Chicago Midway Airport (41.78611°, -87.75222°). Like the Lewis University Airport site, this weather station is located on the operating runway of the airport in the urban core of Chicago. It is located 34.2 km NE of the Lewis Airport site. Both Chicago sites collect the same information as the Baltimore Inner Harbor NOAA stations (https://www.ncdc.noaa.gov/cdo-web/datatools/lcd). Chicago PM2.5 composition data were collected from the Springfield Pump Station (1745 N. Springfield Avenue, Chicago, IL, 60647 41.912526°, -87.722667°) and operated by the Cook County Department of Environmental Control. It is described as an urban site representative of the Chicago-Napierville-Michigan City IL, WI, IN area. This is a state and local monitoring station used for regulatory monitoring purposes. At this site, PM2.5 chemical composition based upon 24-hour filter samples was measured every sixth day (https://www.epa.gov/outdoor-airquality-data). Aerosol Composition Data Aerosol composition data collected at the speciated PM2.5 sample sites were averaged for the months of January and July 2011 to 2015 to provide model inputs for the winter and summer simulations, respectively. Concentration data for Na+, SO42-, NH4+, NO3-, Cl-, Ca2+, K+, and Mg2+ ions in µg m-3 were averaged for each sample in January and July over the five-year period. For January and July in Baltimore, there were 39 and 47 filter samples collected from 2011-


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2015, respectively. For January and July in Chicago, there were 22 and 24 filter samples collected from 2011-2015, respectively. Composition data were not available at the Baltimore urban site or the Chicago rural site, so the available site data were the sole inputs for aerosol composition in the model. The assumption of constant aerosol composition and loading over distances of ~35 km (the distance between the sites) is strongly supported by prior studies in the Baltimore region.46, 48 A similar assumption was applied for the Chicago data to facilitate a parallel analysis. The average summer/winter composition data for Baltimore and Chicago are presented in Supporting Information Tables S2 and S3. The aerosol chemical composition was assumed to be constant throughout the day, based upon the limitations of 24-h filter sampling. Although aerosol inorganic species often show diurnal variations, this assumption provides an opportunity to investigate the effects of the UHI alone on ALW and aerosol pH. Gas-phase Ammonia Data Gas-phase ammonia data were obtained from the National Atmospheric Deposition Program Ammonia Monitoring Network (AMoN, http://nadp.isws.illinois.edu/data/AMoN). In Maryland, the Beltsville CASTNET site is also the location of an AMoN sampling site (MD99), located on the USDA Beltsville Agricultural Research Center east plot (39.0280°, -76.8171°), approximately 6.1 km SE from the HU-Beltsville site. This rural site is situated far from main roads, accessible only from a dirt access road. AMoN samples are collected over 2 weeks using Radiello passive diffusion samplers. In Chicago, two AMoN locations were employed in this analysis, since the AMoN sampling site was not co-located with any meteorological data. Winter data for 2011-2015 from the Bondville site (IL11; 40.0528°, -88.3719°) and for 2012-2015 from the Stockton site (IL37; 42.2869°, -89.9997°), and summer data for 2011-2015 from both sites were collected to establish



a representative regional concentration. The IL11 site is located 232 km SSW of Chicago in agricultural fields outside the city limits of Champaign, IL. The IL37 site is located 229 km ENE of Chicago in agricultural fields west of Freeport, IL. Available AMoN data from January and July were averaged over the same time period used for the aerosol composition averaging (2011 - 2015). Overall, 10 samples for January and 10 samples for July in Baltimore, and 18 samples for January and 20 for July (between the two sites) for Chicago were averaged to provide the winter and summer model NH3 inputs (Supporting Information Tables S2 and S3). Meteorological Data Rural and urban meteorological data were taken from the NOAA (both urban, and Chicago rural) and CASTNET (Baltimore rural) sites described above. For each site, hourly measurements of meteorological conditions are available. For each day in January and July, hourly measurements of air temperature and RH from the most recent three years (2014-2016) were averaged to construct an hourly diurnal profile representative of January and July. There were differences in the aerosol composition (2011-2015) and meteorology (2014-2016) time periods used for averaging. PM composition data represent the most recent 5-year period for which data were available; 2016 data were not yet available at the time the analysis was performed. Additionally, PM composition data were compiled for a longer period given the less frequent sampling in order to determine representative concentrations for each region. Overall, the aerosol and NH3 data and meteorology data provide a good representation of the average summer and winter conditions in the Baltimore and Chicago regions. Characterization of Baltimore and Chicago UHI Several methods are commonly used to characterize the UHI. Remote sensing measurements of land surface temperature can be used to characterize the difference in rural and


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urban land surface temperatures over large spatial scales. These measures are of land surface temperature, or rooftop/horizontal surface temperature in the case of urban environments, and are not the most appropriate measure for studies investigating aerosol chemistry, which is affected by the air temperature. However, land surface temperature (also known as skin temperature) is highly-correlated with air temperature, differing primarily in long timescale variations.49 Highly time-resolved transects measuring air temperature from rural environments through urban environments along a prescribed route to capture the UHI trend were previously used to provide good time resolution and spatial coverage50 but are infrequently used in recent studies. Air temperature measurements can also be used to characterize the UHI by investigating paired urban and rural meteorological station measurements. This is the method most commonly employed to characterize the UHI in observational studies,51 and is the method employed in the present analysis. It should be noted that remotely-sensed UHIs tend to be larger in magnitude and have greater spatial variability by day in comparison to air temperature UHIs, which exhibit a strong diurnal profile that has a maximum intensity at night.3, 52 The urban heat island intensity (UHII), defined as the temperature difference between the urban and rural environments (∆Tu-r), was calculated for each hour throughout the day using the meteorological data described above. The average difference between urban and rural RH values (∆RHu-r) was calculated in the same way as the UHII. Thermodynamic Modeling for Aerosol pH Calculation The compiled composition and meteorological data were used as inputs to the ISORROPIA-II thermodynamic equilibrium model.45 ISORROPIA uses the chemical potential method to solve the equilibrium state after identifying the subsystem of equilibrium equations based on the given inputs. This model was used to determine the equilibrium composition and



phase-partitioning of the NH4+-SO42- -NO3--Cl--Na+-Ca2+-K+-Mg2+ aerosol. Following the approach of Guo et al.,36 the simulations were run in the metastable mode, which prohibits the formation of solid precipitates. ISORROPIA was run in forward mode (gas + aerosol inputs), with inputs of PM2.5 sodium, sulfate, nitrate, chloride, calcium, potassium, and magnesium composition (in µg m-3), along with ammonia gas concentration (µg m-3), and RH and T measured at each site. Note that the NH3 input is the total (NH3 + NH4+), which ISORROPIA then partitions between the gas and particle phases. Similarly, NO3- is total (HNO3 + NO3-), which ISORROPIA partitions between the gas and particle phases. In this analysis, we do not have access to HNO3 data, so the NO3 input is the aerosol NO3- only. Recent studies have shown that aerosol thermodynamic equilibrium models provide more accurate predictions when the inputs include gas-phase measurements.13, 37 Studies in diverse environments show good model performance when the inputs are constrained by either NH337 or HNO3,36 suggesting that the lack of HNO3 data is not detrimental to our analysis. For each month and location, the composition was assumed to be constant at each hourly time point, changing only the meteorological parameters between each model run within a month. The matrix of model simulations and model composition inputs are shown in the Supporting Information (Tables S1-S3). For each city, four paired sets of simulations were run for both January and July: rural using average T and RH, urban using average T and RH, urban using 75th percentile T and RH, and urban using 90th percentile T and RH values.

Thus, for each simulation run of the model, 24 equilibrium

calculations were performed, one average aerosol composition (January or July) simulated at the 24 distinct meteorological conditions throughout the day. The pH of the aerosol was calculated using Equation 2. By utilizing thermodynamic equilibrium models in this manner, it is assumed that the particles are internally mixed, that pH is equal between all particle sizes, and that the


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aerosol is in thermodynamic equilibrium with the gas phase. This calculation method with corresponding assumptions has been shown effective across a wide range of environments.36, 37, 53, 54

Note that we have only considered the aerosol liquid water content of particles associated

with the inorganic species. The exclusion of organics from this calculation is discussed below.

3. Results and Discussion Characterization of Baltimore and Chicago UHI Figure 1 shows the magnitude of the UHII in Baltimore and Chicago (∆Tu-r), and the ∆RHu-r values for July; a similar figure for the January data is presented in the Supplemental Information. (Figure S1). This is computed directly from three years of paired hourly T and RH data from the HU-Beltsville/Downtown Baltimore, and Lewis Airport/Downtown Chicago sites. Scatter plots comparing the urban and rural T and RH for the paired hourly measurements are shown in Supporting Information Figures S2 and S3. The UHII in Baltimore is most intense in July during the night, where an average ∆Tu-r of 4.1°C occurs at 23:00 (LST). In Chicago, the UHII is also strongest in July during the night, where an average ∆Tu-r of 2.7°C occurs at 23:00 (LST). The UHII in Baltimore is least intense in January during the afternoon, where there was no difference in the average urban and rural temperatures between 14:00 – 16:00 (LST). The UHII in Chicago is also least intense in January during the afternoon, where an average ∆Tu-r of 0.75°C occurs at 10:00 (LST). The magnitude of the Baltimore UHII is consistent with past studies of the Baltimore-Washington UHI.4, 11, 52, 55 The UHI in both Baltimore and Chicago is almost always present during the summer, as the urban T was systematically higher than the rural T for most hourly measurements across three years (Figures S2 and S3). The daily UHII average in Baltimore during July is 2.8 °C, but Figure 1 shows that the urban-rural temperature difference is frequently much greater than this; the 90th percentile ∆Tu-r is greater than 6 °C at night. For



Chicago, the daily UHI average during July is 1.8 °C, and displays less variance than that in Baltimore. The ∆RHu-r profiles for both cities show the expected trend; during midday, when the temperature is highest (and when the UHII is lowest), the ∆RHu-r trends towards 0, and shows the greatest difference (highest rural RH) at night when the ∆Tu-r is at its maximum. During the summer in Baltimore, the average RH difference between the rural and urban sites was 10.1% (RH scale), but regularly exceeded 25%. For more than 90% of hourly measurements across three years, the Baltimore rural RH exceeded the urban RH (Fig. S2), consistent with the systematic temperature differences. Chicago shows a very similar trend in average RH difference between the urban and rural sites, but the systematic differences between rural and urban RH values were less pronounced than that in Baltimore during July. In January, the UHI is frequently present in Baltimore, but is much less intense than during the summer. The average ∆Tu-r was 1.2 °C, while the average ∆RHu-r was 0.3% (RH units). There are periods in the late morning to evening (10:00 to 19:00) where the 10th and 25th percentile ∆Tu-r and ∆RHu-r values, and occasionally the median fall below 0 (for ∆Tu-r) or rise above 0 (∆RHu-r), indicating that the rural environment is frequently warmer and less humid than the urban environment. This suggests that the UHI affects aerosol chemistry far more during summer than winter in Baltimore. For Chicago, there is a persistent UHI effect present throughout the day, with an average value of 1.1 °C, leading to a systematic difference in ∆RHu-r well (Figure S3). As in Baltimore, this suggests that the UHI affects aerosol chemistry far more during summer than winter. UHI Effects on Aerosol Liquid Water Figure 2 shows average diurnal profiles of aerosol liquid water (ALW) for Baltimore and Chicago during July. In both winter and summer there is a noticeable diurnal trend in the ALW, however, the magnitude is vastly reduced in the winter in both locations (Figure S4). This


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difference is consistent with seasonal trends in ALW observed across the U.S.56 During both winter and summer periods, there is good qualitative agreement between the observed diurnal profile for Baltimore and that observed in the southeastern U.S. during the SOAS campaign.37 Overall, the greatest ALW values occur for the rural environments in July during the night, directly corresponding to the periods of greatest UHII and lowest ∆RHu-r. During both summer and winter, the ALW content decreased in the urban environments as the UHII increased. Under average summertime meteorological conditions, the rural ALW content was almost twice as high as the urban ALW content at night in both Baltimore and Chicago. When higher T and RH differences were observed, the difference in ALW content was even greater: for example, at the 90th percentile ∆Tu-r, the rural ALW content in Baltimore was more than a factor of three higher than the urban ALW. The daytime ALW contents were much closer: under average meteorological conditions, daytime ALW content at the Baltimore rural site was only ~20% higher than ALW content at the urban site, and ~15% higher for the Chicago rural site. In contrast to the summertime, differences in ALW between the rural and urban environments were much smaller during January. This was due to the smaller UHII and smaller ∆RHu-r during the winter. The Baltimore results in Fig. 2 are in good agreement with another study in the southeastern U. S., which found systematically higher ALW in rural areas compared to urban areas.57 The authors postulate that the differences were driven by the UHI, and our results strongly support this hypothesis. UHI Effects on Aerosol pH Figure 3 shows diurnal profiles of the modeled aerosol pH for the Baltimore and Chicago in July. Following the trends shown in Figures 1 and 2, aerosol pH shows a pronounced diurnal pattern. The modeled aerosol chemical composition was assumed constant throughout the day



(Supporting Information Tables S2 and S3), so the diurnal changes in pH were attributed solely to the changing meteorology, and thus ALW content. Consistent with the urban-rural gradient in ALW content shown in Fig. 2, the largest difference between urban and rural aerosol pH occurs in July. During July, the rural pH values are systematically higher (less acidic) than the urban pH values. In addition, the difference in pH between the urban and rural sites increases as the UHII increases. Aerosol pH in both the urban and rural locations is strongly affected by both T and RH. At night and early morning, higher ALW values have a diluting effect on the aerosol, resulting in higher pH (lower aqueous H+ concentration); during the day, the opposite trend is observed, where reduced ALW has a concentrating effect on the aqueous H+ concentration resulting in a lower pH. The modeled pH values are in good agreement with those obtained during the SOAS study in a rural area in the southeastern U.S.37 Note that Guo et al.37 used highly time resolved aerosol and gas-phase measurements for their model inputs, so the strong correlations (R2 = 0.70 for urban pH, R2 = 0.65 for rural pH) between our modeled pH values demonstrates the important influence of meteorology on aerosol pH. It should be emphasized that the comparisons shown in Fig. 3 were predicted using the same aerosol chemical composition and concentrations (although different between seasons), so these differences in aerosol pH are due solely to meteorological differences associated with the UHI. An analysis of the H+air concentration (in µg m-3; see Equation 2, analysis not shown) reveals a diurnal trend, but no multi-fold increase in the predicted concentration of the H+air ion at the urban sites compared to the rural sites. This indicates that the differences in ALW between the urban and rural sites (Fig. 2) is driving the differences in pH. The ∆pHr-u values follow similar trends to ALW content (Figures S6 and S7). The urban aerosol in Baltimore and Chicago is systematically more acidic than the rural aerosol during both


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seasons, but the difference is much more pronounced during summer. During July, for the 90th percentile ∆Tu-r, the Baltimore urban aerosol has a pH that is lower by 0.2-0.9 pH units, and the Chicago urban aerosol has a pH that is lower 0.2 to 0.7 pH units, depending on the time of day. During January, the Baltimore urban aerosol pH is lower by only 0.06-0.4 pH units at the 90th percentile ∆Tu-r; for Chicago this difference is 0.07-0.16 pH units. The ∆pHr-u values show strong diurnal profiles, with maxima at night and minimum values in the mid-late afternoon for both January and July (Supporting Information Figs. S6 and S7). Figures 1-3 show that the difference in pH between urban and rural areas is controlled by the difference in ALW, itself tightly linked to T and RH. Figure 4 shows how the pH difference between the urban and rural locations is a function of the T and RH differences. The ∆pHr-u was characterized as a function of the ∆Tu-r and ∆RHr-u (note here the change in difference method to present similar trends) for January and July. Both the ∆Tu-r and the ∆RHr-u values show strong capabilities in predicting the aerosol pH differences between the urban and rural sites. These correlation results are surprising given the major differences in atmospheric composition and meteorology between January and July and between Baltimore and Chicago. The overlap in ∆pHr-u, ∆Tu-r, and ∆RHr-u values across January and July suggests that meteorology is driving the ∆pHr-u more strongly than the change in seasonal composition. Implications The present results show that the UHI can have a pronounced effect on aerosol pH, primarily through its effect on the particle liquid water content. The aerosol pH was systematically more acidic in the urban environment than in surrounding rural/suburban environments for the same aerosol and gas-phase composition. This effect is a strong function of the temperature and RH gradients that characterize the UHI. For Baltimore and Chicago, the



differences in pH are most pronounced in the summer, at night, when the UHII is observed to be the greatest. However, while the UHI is a widely observed phenomenon it can take different forms – different magnitudes, different diurnal profiles – in different cities. In some cases, this can lead to the UHI being highest during winter.2 While this UHI effect on aerosol pH holds for Baltimore and Chicago, the seasonal and diurnal differences may need to be characterized for each city. A key assumption in our study is that aerosol composition was identical between the urban and rural/suburban environments. For the Baltimore region, this is a good assumption,46 but this has not been evaluated for Chicago. The species that drive aerosol pH – sulfate, nitrate, and ammonium – are often regional in nature,58-61 so similar urban-rural concentrations are likely over spatial scales of ~1-30 of km in the vicinity of many cities. This assumption was made to assess the impact of the UHI alone on the aerosol pH. However, the UHI effect (transition from rural to urban environment), along with other warming effects, have been shown to increase aerosol concentrations, for present studies and future predictions.5, 8, 62-66 Thus, while the assumption of identical composition in both rural and urban environments enables our characterization of UHI impact on aerosol pH, there is also a potentially important change in aerosol composition driving changes in aerosol pH that may occur simultaneously with the UHI effect. We performed a simple sensitivity analysis to quantify the difference in sulfate concentrations that would be required to produce the same ∆pH values that are predicted to arise from the UHI. For this analysis, we used the average rural meteorological conditions, and increased the amount of sulfate present in the ISORROPIA inputs while leaving the other concentrations unchanged, and simulating this change until the ∆pHr-u values at all time points


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moved from positive to negative (results not shown). This analysis reveals that, if the urban and rural meteorology were identical, the ∆pHr-u values from the UHI effect are equivalent to an approximately two-fold increase in the sulfate concentration for both Baltimore (2.63 to 4.25 µg m-3) and Chicago (2.6 to 4.75 µg m-3). This underscores the potential of the UHI to dramatically influence aerosol chemistry in and around urban atmospheres. We anticipate that the general trends observed in this study will translate widely to other urban areas. This is supported by the results of Fig. 4, which shows a strong predictive relationship between ∆pHr-u and ∆Tu-r, even though the seasons and locations had very different aerosol compositions and meteorology. However, this phenomenon should be investigated across even more diverse urban areas, as well. If highly time-resolved aerosol and gas-phase composition measurements are available, it would also be advantageous to characterize differences in urban-rural pH that may result from simultaneous changes in composition and meteorology. Several past field campaigns are well-positioning for this type of analysis, having employed multiple field sites (rural/suburban, urban, urban downwind) to collect highly timeresolved aerosol composition measurements in paired urban-rural locations.67, 68 From Equation 2, only the ALW associated with the inorganic aerosol species was used in the calculation of pH due to limitations in the available organic aerosol data. Guo et al. found a systematic under prediction of pH by 0.15-0.23 pH units when the ALW associated with organics (ALWo) was omitted.36 Because our study excludes this fraction, we acknowledge that the aerosol pH values shown in Figures 3 and S5 are likely different from more precise estimates that would be obtained by including this water. However, the results of Guo et al.37 strongly support our approach, since their aerosol pH values calculated with and without the ALW



associated with organics were highly similar (slope = 0.94, intercept = -0.14) and were so strongly correlated (R2 = 0.97). More than half the world’s population lives in urban environments, and this is expected to grow to over 80% by 2030.52 Under conditions of a warming climate, UHI intensity is likely to increase, as well. Therefore, understanding the effect of the UHI on atmospheric chemistry is critical owing to the role of atmospheric pollution in human and ecological health. Because the UHI is such a widely observed phenomenon, we expect the results of this study to inform processes occurring in and around urban environments globally. For example, the results of this study suggest a lower potential to form aqueous secondary organic aerosol (aqSOA) in urban areas due to lower ALW content. The transformation and fate of anthropogenic VOCs could thus be strongly impacted. This work may also have implications for the toxicity of urban aerosols, since pH-mediated dissolution of metals can contribute to the generation of reactive oxygen species.15 It is important to note that the changes in aerosol chemistry that accompany the UHI likely occur on scales that are not resolved by most regional or global models, which are often challenged to predict urban aerosol levels.69 However, the Weather Research and Forecasting (WRF) model, which is widely used to simulate meteorology in global and regional models, can be used to improve representations of the UHI.70 This may be necessary to accurately capture the changes in aerosol chemistry that accompany the meteorological phenomenon of the urban heat island.

4. Supporting Information Supporting information for this document is available. The Supporting information includes a summary of all the model simulations run, along with the aerosol chemical composition and


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meteorological data for model inputs. Supplemental figures include: diurnal profiles of the ∆Tu-r and ∆RHu-r profiles for Chicago and Baltimore during January; scatter plots of hourly T and RH comparing urban and rural sites in Chicago and Baltimore; average diurnal profiles of ALW and aerosol pH during January.

5. Acknowledgements This work was supported by the National Science Foundation through grant CHE-1454763.



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Figure 1: Diurnal profiles of ∆Tu-r and ∆RHu-r for July in Baltimore and Chicago. Whiskers represent 10th and 90th percentile values, boxes represent quartiles, box midline represents median value, and colored diamonds represent the mean. Identical winter data is shown in the Supplemental Information (Fig. S1).



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Figure 2: Diurnal profiles of aerosol liquid water content predicted by ISORROPIA-II for identical aerosol concentration inputs and four differing cases of meteorological inputs. Results shown are for Baltimore and Chicago July data. January data is shown in the Supplemental Information (Fig. S4).


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Figure 3: Diurnal profiles of modeled aerosol pH calculated from Equation 2 using the outputs of ISORROPIA-II. Results shown are for Baltimore and Chicago July data. January data is shown in the Supplemental Information (Fig. S5).



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Figure 4: Scatterplots of the calculated difference in rural – urban pH (∆pHr-u) vs. the paired UHII (∆Tu-r) (a) and ∆RHr-u values (b), separated by season (Baltimore January data in blue, Baltimore July in red, Chicago January in yellow, Chicago July in green). A 2nd order polynomial in the form a2x2 + a1x + a0 was fit to the entire dataset (January and July).


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Effect of the Urban Heat Island on Aerosol pH - Environmental Science

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