Urbanization has a profound influence on regional meteorology and air quality
in megapolitan Southern California. The influence of urbanization on
meteorology is driven by changes in land surface physical properties and land
surface processes. These changes in meteorology in turn influence air quality
by changing temperature-dependent chemical reactions and emissions,
gas–particle phase partitioning, and ventilation of pollutants. In this study
we characterize the influence of land surface changes via historical
urbanization from before human settlement to the present day on meteorology and
air quality in Southern California using the Weather Research and Forecasting
Model coupled to chemistry and the single-layer urban canopy model
(WRF–UCM–Chem). We assume identical anthropogenic emissions for the
simulations carried out and thus focus on the effect of changes in land
surface physical properties and land surface processes on air quality.
Historical urbanization has led to daytime air temperature decreases of up to
1.4 K and evening temperature increases of up to 1.7 K. Ventilation of air
in the LA basin has decreased up to 36.6 % during daytime and increased
up to 27.0 % during nighttime. These changes in meteorology are mainly
attributable to higher evaporative fluxes and thermal inertia of soil from
irrigation and increased surface roughness and thermal inertia from
buildings. Changes in ventilation drive changes in hourly
The world has been undergoing accelerated urbanization since the industrial revolution in the 19th century (Grimm et al., 2008; Seto et al., 2012). Urbanization leads to profound human modification of the land surface and its associated physical properties such as roughness, thermal inertia, and albedo (Fan et al., 2017) and land surface processes like irrigation (Vahmani and Hogue, 2014). These changes in land surface physical properties and processes alter corresponding surface–atmosphere coupling including exchange of water, momentum, and energy in urbanized regions (Vahmani and Ban-Weiss, 2016a; Li et al., 2017), which exerts an important influence on regional meteorology and air quality (Vahmani et al., 2016; Civerolo et al., 2007).
Land surface modifications from urbanization drive changes in urban meteorological variables such as temperature, wind speed, and planetary boundary layer (PBL) height, which result in urban–rural differences. Differences in surface temperature and near-surface air temperature have been widely studied for decades. The urban heat island (UHI) effect, a phenomenon in which temperatures within an urban area are higher than surrounding rural areas (Oke, 1982), has been extensively studied using models and observations for a great number of urban regions (Rizwan et al., 2008; Peng et al., 2012; Stewart and Oke, 2012). A contrary phenomenon, namely the urban cool island (UCI), under which urban temperatures are lower than surrounding rural temperatures, has also been investigated recently in some studies (Carnahan and Larson, 1990; Theeuwes et al., 2015; Kumar et al., 2017). Urban–rural contrast in temperature (i.e., both UHI and UCI) is mainly attributable to differences in thermal properties and energy fluxes due to heterogeneous land surface properties. For urban areas, buildings and roads (i.e., impervious surfaces) are generally made from manufactured materials (e.g., asphalt concrete) with low albedo and thus high solar absorptivity (Wang et al., 2017). These materials also have high thermal inertia, which can lead to reductions in diurnal temperature range due to heat storage and consequent temperature reductions during the day and heat release and consequent temperature increases at night (Hardin and Vanos, 2018). Street canyons, which we refer to as the U-shaped region between buildings, can trap longwave energy fluxes within the canyon because of reductions of sky-view factors (Qiao et al., 2013). Conversely, shading in street canyons during the day can reduce absorption of shortwave radiation (Carnahan and Larson, 1990; Kusaka et al., 2001). Pervious surfaces within urban areas such as irrigated urban parks and lawns can lead to the urban oasis effect in which evaporative cooling occurs due to increases in evapotranspiration. In addition, soil thermal properties depend on their water content, which ultimately affects ground heat fluxes and thus surface and air temperatures. Land surface properties in surrounding rural areas can also affect urban–rural differences in temperature (Imhoff et al., 2010; Peng et al., 2012; Zhao et al., 2014). In particular, urban regions built in semiarid or arid surroundings tend to have a weak daytime UHI or even a UCI, whereas those built in moist regions tend to have a larger daytime UHI (Fan et al., 2017; Peng et al., 2012). Lastly, factors such as anthropogenic heat and atmospheric aerosol burdens can play an important role in urban heat or cool island formation in some regions (Oke, 1982; Wang et al., 2017).
Urbanization can also cause differences between urban and rural areas for meteorological variables other than surface and air temperatures. Changes in regional near-surface wind speed and direction can occur in urban areas because of spatially varying modifications in surface roughness (Xu et al., 2006; Vahmani et al., 2016). Changes in near-surface winds in coastal urban areas can also be affected by modifying land–sea temperature contrast (Vahmani et al., 2016). The characteristics of the PBL are dependent on the magnitude of turbulent kinetic energy (TKE) (Garratt, 1994). Higher (lower) TKE will lead to deeper (shallower) PBLs. During daytime, the magnitude of TKE is driven by buoyancy production contributed mainly by sensible heat flux (with clear skies); at night, TKE is driven by shear production associated with variance in wind speed. Thus, temperature and surface roughness play an important role in the depth of the PBL during daytime and nighttime, respectively. Lastly, changes in relative humidity, precipitation, and other meteorological variables due to land surface changes can also be significant in some regions (Burian and Shepherd, 2005; Georgescu et al., 2014).
Changes in meteorological conditions due to urbanization can influence
concentrations of air pollutants including oxides of nitrogen
(
A number of previous studies have investigated the impacts of land surface
changes on regional meteorology in a variety of urban regions around the
world (e.g., Kalnay and Cai, 2003; Burian and Shepherd, 2005; Zhang et al., 2010).
However, limited studies have quantified the impact of land surface changes
on regional air quality, and those that do exist have focused on only changes in
surface
With advances in real-world land surface datasets from satellites, recent modeling studies on land–atmosphere interactions are able to resolve heterogeneous land surface properties and thus better capture urban meteorology, enabling modeling studies that more accurately quantify changes in regional meteorology due to land surface modification. By combining satellite-retrieved high-resolution land surface data with the Weather Research and Forecasting Model coupled to the single-layer urban canopy model (WRF–UCM), simulations reported in Vahmani and Ban-Weiss (2016a) show improved model performance (i.e., compared to observations) for meteorology in Southern California compared to the default model, which assumes that urban regions have homogeneous urban land cover. In a follow-up study, Vahmani et al. (2016) suggested that historical urbanization has altered regional meteorology (e.g., near-surface air temperatures and wind flows) in Southern California mainly because of urban irrigation and changes in land surface thermal properties and roughness. While historical urbanization and its associated impacts on meteorology have the potential to cause important changes in air pollutant concentrations in Southern California, this has never been investigated in past work.
Therefore, this study aims to characterize the influence of land surface changes via historical urbanization on urban meteorology and air quality in Southern California by comparing a “present-day” scenario with current urban land surface properties and land surface processes to a “nonurban” scenario assuming land surface distributions prior to human perturbation. To achieve this goal, we adopt a state-of-the-science regional climate–air quality model, the Weather Research and Forecasting Model coupled to chemistry and the single-layer urban canopy model (WRF–UCM–Chem), and incorporate high-resolution heterogeneity in urban surface properties and processes to predict regional weather and pollutant concentrations. We assess the response of regional meteorology and air quality to individual changes in land surface properties and processes to determine driving factors on atmospheric changes. Note that this paper builds on our prior study Vahmani et al. (2016) but focuses on air quality impacts, whereas our previous research was on meteorological impacts only. While the influence of land surface changes on regional weather has been investigated in numerous past studies, its influence on regional air quality has been seldom studied in past work. In this paper, we aim to quantify the importance of historical land cover change on air pollutant concentrations, and thus the nonurban scenario assumes current anthropogenic pollutant emissions. This hypothetical scenario cannot exist in reality since current anthropogenic emissions would not exist without the city, but our intent is to tease out the relative importance of land cover change through urbanization (assuming constant emissions) on air pollutant concentrations.
WRF-Chem v3.7 is used in this study to simulate meteorological fields and atmospheric chemistry. WRF-Chem is a state-of-the-science nonhydrostatic mesoscale numerical meteorological model that facilitates “online” simulation of processes relevant to atmospheric chemistry including pollutant emissions, gas- and particle-phase chemistry, transport and mixing, and deposition (Grell et al., 2005). In this study, we activate the urban canopy model (UCM) in WRF-Chem that resolves land–atmosphere exchange of water, momentum, and energy for impervious surfaces in urban areas (Kusaka et al., 2001; Chen et al., 2011; Yang et al., 2015). The UCM parameterizes the effects of urban geometry on energy fluxes from urban facets (i.e., roofs, walls, and roads) and wind profiles within canyons (Kusaka et al., 2012). We account for the effect of anthropogenic heat on urban climate by adopting the default diurnal profile in the UCM. We used UCM instead of the multilayer canopy layer model (BEP) because BEP would increase computational costs, but for likely little additional benefit in the quality of simulations (Chen et al., 2011; Kusaka et al., 2001). Physics schemes included in our model configuration are the Lin cloud microphysics scheme (Lin et al., 1983), the RRTM longwave radiation scheme (Mlawer et al., 1997), the Goddard shortwave radiation scheme (Chou and Suarez, 1999), the YSU boundary layer scheme (Hong et al., 2006), the MM5 similarity surface layer scheme (Dyer and Hicks, 1970; Paulson, 1970), the Grell 3-D ensemble cumulus cloud scheme (Grell and Dévényi, 2002), and the unified Noah land surface model (Chen et al., 2001). Chemistry schemes include the TUV photolysis scheme (Madronich, 1987), RACM-ESRL gas-phase chemistry (Kim et al., 2009; Stockwell et al., 1997), and MADE/VBS aerosol scheme (Ackermann et al., 1998; Ahmadov et al., 2012).
All model simulations are carried out from 28 June, 21:00 UTC (28 June,
13:00 PST) to 8 July, 07:00 UTC (7 July, 23:00 PST), 2012 using the North
American Regional Reanalysis (NARR) dataset as initial and boundary
meteorological conditions (Mesinger et al., 2006). This simulation period is
chosen as representative of typical summer days in Southern California, which
are generally clear or mostly sunny without precipitation. A comparison of
observed diurnal cycles for average near-surface air temperatures over JJA
(June, July, and August) in the year 2012 versus over our simulation period is
shown in Fig. S8 in the Supplement. Hourly model output from
1 July, 00:00 PST to 7 July, 23:00 PST is used for analysis, and simulation
results prior to 1 July, 00:00 PST are discarded as spin-up. Figure 1a
shows the three two-way nested domains with horizontal resolutions of 18, 6,
and 2 km centered at 33.9
Maps of
One important aspect of accurately simulating meteorology and air quality is to properly characterize land surface–atmosphere interactions (Vahmani and Ban-Weiss, 2016a; Li et al., 2017). In addition, accurately quantifying the climate and air quality impacts of historical urbanization requires a realistic portrayal of current land cover in the urban area (Vahmani et al., 2016). For both of these reasons, we update the default WRF-Chem to include a real-world representation of land surface physical properties and processes.
In this study, we use the (30 m resolution) 33-category National Land Cover Database (NLCD) for the year 2006 for all three model domains. NLCD differentiates three urban types including low-intensity residential, high-intensity residential, and industrial/commercial (shown in Fig. 1b) (Fry et al., 2011). In the model (UCM), each of these three types can have unique urban physical properties such as building morphology, albedo, and thermal properties for each facet. We adopt the grid-cell-specific National Urban Database and Access Portal Tool (NUDAPT) where available in the innermost domain for building morphology including average building heights, road widths, and roof widths (Ching et al., 2009). Where NUDAPT data are unavailable, we use average building and road morphology for three urban categories from the Los Angeles Region Imagery Acquisition Consortium (LARIAC). Details on the generation of averaged urban morphology parameters from real-world GIS datasets can be found in Zhang et al. (2018a). For the other parameters in the UCM (e.g., anthropogenic latent heat, surface emissivity), we use default WRF settings documented in file URBPARM.TBL. Note that the original gaseous dry deposition code based on Wesely (1989) is only compatible with the default 24-category US Geological Survey (USGS) global land cover map. We therefore modify the code according to Fallmann et al. (2016), which assumes that the three urban types in the 33-category system have input resistances that are the same as the urban type for the 24-category system. In addition, impervious fractions (i.e., the fraction of each cell covered by impervious surfaces) for each of the three urban categories in the innermost domain are from the NLCD impervious surface data (Wickham et al., 2013).
Land surface properties including albedo, green vegetation fraction (GVF),
and leaf area index (LAI) are important for accurately predicting absorption
and reflection of solar radiation and evaporative fluxes in urban areas
(Vahmani and Ban-Weiss, 2016a). To resolve high-resolution real-world
heterogeneity in these land surface properties, the simulations performed in
this study use satellite-retrieved real-time albedo, GVF, and LAI for the
innermost domain. Input data compatible with WRF are regridded horizontally
using albedo, GVF, and LAI maps generated based on MODIS
reflectance (MCD43A4), vegetation indices (MOD13A3), and fraction of
photosynthetically active radiation (MCD15A3) products, respectively. Raw
data are available from the USGS National Center for Earth Resource
Observations and Science website at
Resolving urban irrigation is also of great significance for accurately predicting latent heat fluxes and temperatures within Southern California. Here we use an irrigation module developed by Vahmani and Hogue (2014), which assumes irrigation occurs three times a week at 21:00 PST on the pervious fraction of urban grid cells. This model was tuned to match observations of evapotranspiration in the Los Angeles area. Details on the implementation of this irrigation module and its evaluation with observations can be found in Vahmani and Hogue (2014). Note that we do not use the default irrigation module available in the single-layer canopy model in WRF–UCM v3.7, which assumes daily irrigation at 21:00 PST in summertime, because (1) the irrigation module of Vahmani and Hogue (2014) was already evaluated and tuned for Southern California, and (2) we strive to maintain consistency with our previous related studies.
Producing accurate air quality predictions also relies on using emission
inventories that capture real-world emissions. We adopt year 2012
anthropogenic emissions from the California Air Resource Board (CARB) for the
two outer domains (CARB, 2017) where data are available (i.e., within
California) and from the South Coast Air Quality Management District (SCAQMD)
for the innermost domain (SCAQMD, 2017). For areas within the two outer
domains that are outside California, we use the US Environmental Protection
Agency (EPA) National Emissions Inventory (NEI) for 2011 that is available
with the standard WRF-Chem model (US EPA, 2014). CARB and SCAQMD emission
inventories as provided have 4 km spatial resolution, with 18 and 11 layers
in the vertical direction from the ground to 100 hPa, respectively. We regridded these
inventories in the horizontal and vertical directions to match the grids of our modeling
domains. Note that the aforementioned emission inventories use chemical
speciation from the SAPRC chemical mechanism (Carter, 2003), and thus we have
converted species to align with the RACM-ESRL and MADE/VBS mechanisms, both
of which use RADM2 (Regional Acid Deposition Model) speciation (Stockwell et
al., 1990). The conversion uses species and weighting factors from the
emiss_v04.F script that is distributed with NEI emissions for WRF-Chem
modeling. (The original script is available at
To facilitate model evaluation, we obtain hourly near-surface air temperature
observations, hourly ground-level
To investigate the effects of land surface changes via historical
urbanization on regional meteorology and air quality in Southern California,
we carry out two main simulations, which we refer to as the present-day
scenario and nonurban scenario. The two scenarios differ only by the
assumed land surface properties and processes, which are shown in Fig. 2. The
present-day scenario assumes the current land cover (Fig. 1b) and irrigation for Southern California (described in Sect. 2.2). Urban morphology
from NUDAPT and LARIAC and MODIS-retrieved albedo, GVF, and LAI are used in
this scenario. To help explain the impact of urbanization without the
addition of irrigation, a supplemental simulation, which we refer to as
“present-day no-irrigation”, is also carried out; this simulation is
identical to the present-day scenario but assumes that there is no irrigation. For the
nonurban scenario, we assume natural land cover prior to human perturbation,
and replace all urban grid cells with “shrubs” (Fig. 1c). We modify
MODIS-retrieved albedo, GVF, and LAI in these areas based on properties for
shrublands surrounding urban regions in the present-day scenario. A detailed
explanation on this method (inverse distance weighting approach) can be found
in Vahmani et al. (2016). The spatial patterns of land surface properties in
both present-day and nonurban scenarios are shown in Fig. S10 in the Supplement. Note
that all three aforementioned scenarios adopt identical anthropogenic
emission inventories described in Sect. 2.3. Using current anthropogenic
emissions for the nonurban scenario is a hypothetical scenario that cannot exist in
reality but allows us to tease out the effects of land surface changes via
urbanization on meteorology and air pollutant concentrations. Biogenic
emissions do change for the scenarios due to changes in land surface
properties (e.g., vegetation type and LAI) and meteorology (e.g.,
temperature). To check whether the changes in regional meteorology and air
quality due to land surface changes are distinguishable from zero,
statistical significance at the 95 % confidence interval is tested using the
paired Student's
Spatial patterns of differences (present-day
Note that the results reported in this paper are based on model simulations and are thus dependent on how accurately the regional climate–chemistry model characterizes the climate–chemistry system (e.g., meteorology, surface-atmosphere coupling, and atmospheric chemical reactions). Results may be dependent on model configuration (e.g., physical and chemical schemes), land surface characterizations (e.g., satellite data from MODIS, or default dataset available in WRF), and emission inventories (e.g., anthropogenic emission inventories from CARB, SCAQMD, or NEI). In addition, since irrigation is not included in the nonurban scenario, simulated meteorology in the nonurban scenario is dependent on assumed initial soil moisture conditions. In this study, we adopt the initial soil moisture conditions from Vahmani et al. (2016) for consistency with our previous work. Soil moisture initial conditions are based on values from 6-month simulations without irrigation (Vahmani and Ban-Weiss, 2016b).
In this section, we focus on the predicative capability of the model for
simulated near-surface air temperature, not including dust emissions in the simulation, which makes up an
appreciable fraction of real-world total PM while the
observations measure values for one single point near the surface, model
values represent a grid cell average with a larger spatial “footprint”.
Note that the focus of this study is on the changes in pollutant
concentrations, and thus differences between simulations are of increased interest
relative to absolute values. Table 1 shows four statistical metrics for model
evaluation, including mean bias (MB) and normalized mean bias (NMB) for the
quantification of bias and mean error (ME) and root-mean-square error (RMSE)
for the quantification of error. The statistical results indicate that model
simulations underestimate near-surface air temperature,
Comparison between modeled and observed
Summary statistics (mean bias, MB; normalized mean bias, NMB; mean
error, ME; and root-mean-square error, RMSE) for model evaluation, which
compares modeled (abbreviated as “mod” in the footnote) hourly near-surface air temperature (
The effects of land surface changes via urbanization in Southern California
on air temperature and ventilation coefficient are discussed in this section.
Air temperatures are reported for the lowest atmosphere model layer rather
than the default diagnostic 2 m (near-surface) air temperature variable to
be consistent with reported air pollutant concentrations shown in later
sections. (The chemistry code makes use of grid cell air temperature and does
not use 2 m air temperature.) Ventilation coefficient is calculated as the
product of PBL height and the average wind speed within the PBL and thus
considers the combined effects of vertical and horizontal mixing and
indicates the ability of the atmosphere to disperse air pollutants (Ashrafi
et al., 2009). This calculation can be written as Eq. (1).
As shown in Fig. 4a, our simulations suggest that urbanization in Southern California has in general led
to urban temperature reductions during daytime from 07:00 to 16:00 PST and
urban temperature increases during other times of day. The largest spatially
averaged temperature reduction occurs at 10:00 PST (
Diurnal cycles for present-day (red), nonurban (blue), and
present-day
During the morning, temperature reductions are larger in regions further away
from the sea (e.g., San Fernando Valley and Riverside County) than coastal
regions (e.g., west Los Angeles and Orange County) (Fig. 5a). (Note that
regions that are frequently mentioned in this study are in Fig. 2a.) Spatial
patterns in the afternoon are similar to morning, with the exception that
coastal regions experience temperature increases (as opposed to decreases) of
up to
Spatial patterns of differences (present-day
The temporal and spatial patterns of air temperature changes suggest that the climate response to urbanization during daytime is mainly associated with the competition between (a) temperature reductions from increased evapotranspiration and thermal inertia from urban irrigation and (b) temperature increases from decreased onshore sea breezes (Supplement Fig. S14d, e). Decreases in the onshore sea breeze are primarily caused by increased roughness lengths from urbanization. (Note that the onshore sea breeze decreases in strength despite higher temperatures in the coastal region of Los Angeles during the afternoon, which would tend to increase the land–sea temperature contrast and thus be expected to increase the sea breeze strength.) Inland regions show larger temperature reductions relative to coastal regions because they have lower urban fractions (Fig. S10a in the Supplement), and thus higher pervious fractions. Since irrigation increases soil moisture in the pervious fraction of the grid cell in this model, irrigation will have a larger influence on grid-cell-averaged latent heat fluxes (Fig. S15 in the Supplement) and thermal inertia when pervious fractions are higher. The inland regions are also less affected by changes in the sea breeze relative to coastal regions since they are (a) farther from the ocean and (b) experience smaller increases in roughness length. Roughness length effects on the sea breeze are especially important in the afternoon when baseline wind speeds are generally highest in the Los Angeles basin. Thus, the afternoon temperature increases simulated in the coastal region occur because temperature increases from reductions in the afternoon onshore flows dominate over temperature decreases from increased evapotranspiration. In addition, increases in thermal inertia caused by use of manmade materials (e.g., pavements and buildings) can contribute to simulated temperature reductions during the morning. Please see Supplement Sect. S3 for the additional simulation (present-day no-irrigation scenario) carried out to identify the influence of urbanization but without changing irrigation relative to the nonurban scenario (i.e., with no irrigation).
Note that changes in air temperature during daytime shown here disagree with Vahmani et al. (2016). While our study detects daytime temperature reductions due to urbanization, Vahmani et al. (2016) suggests daytime warming. After detailed comparison of the simulations in our study versus Vahmani et al. (2016), we find that the differences are mainly associated with UCM configuration. First, our study uses model default calculations of surface temperature for the impervious portion of urban grid cells, whereas Vahmani et al. (2016) applied an alternative calculation proposed by Li and Bou-Zeid (2014). Li and Bou-Zeid (2014) intended the alternate surface temperature calculations to be performed as a post-processing step rather than during runtime. After a careful comparison among different model setups, we find that the parameterization of surface temperature is an important factor that affects simulated daytime air temperature (see Fig. S16 in the Supplement). Second, our study accounts for shadow effects in urban canopies, whereas Vahmani et al. (2016) assumes no shadow effects. (We note here that the default version of the UCM has the shadow model turned off. The boolean SHADOW variable in module_sf_urban.F needs to be manually switched to true to enable the shadow model calculations. With the shadow model turned off, all shortwave radiation within the urban canopy is assumed diffuse.) We suggest that it is important to include the effects of building morphology on shadows within the canopy and to track direct and diffuse radiation separately, and we therefore perform simulations in this study with the shadow model on. Note that the effect of shadows is not as significant as the parameterization of surface temperature for most of the domain in our study because the ratio between building height and road width is small.
The climate response to urbanization during nighttime is driven by the combined effects of (a) temperature increases from increasing upward ground heat fluxes and (b) temperature increases from increasing PBL heights. Increased soil moisture (from irrigation) and use of anthropogenic materials leads to higher thermal inertia of the ground; this in turn leads to increased heat storage during the day and higher upward ground heat fluxes and thus surface temperatures at night. Increasing PBL heights can also lead to warming because of lower air cooling rates during nighttime. Changes in PBL heights are associated with surface roughness changes since shear production dominates TKE at night. Coastal (inland) regions show larger (smaller) spatial variation in roughness length (Fig. 2e), which leads to larger (smaller) increases in PBL heights (Fig. S14c in the Supplement). Despite larger increases in PBL heights in coastal versus inland regions, smaller air temperature increases occur in coastal versus inland regions, likely due to accumulative effects from coastal to inland regions with onshore wind flows.
Changes in ventilation coefficient show a similar temporal pattern as air
temperature (Fig. 4b); values decrease by up to
At night, spatially averaged ventilation coefficient increases by
Concentrations of pollutants are profoundly impacted by meteorological
conditions including air temperature and the ventilation capability of the
atmosphere (Aw and Kleeman, 2003; Rao et al., 2003). This section discusses
how meteorological changes due to land surface changes via urbanization in
Southern California affect gaseous pollutant concentrations (i.e.,
As shown in Fig. 6a, changes in meteorological fields due to urbanization
have led to increases in hourly
Diurnal cycles for present-day (red), nonurban (blue), and
present-day
Figure 7a, b, c show the spatial patterns of
Spatial patterns in differences (present-day
The spatial patterns of changes in
As indicated by Fig. 6b,
Figure 7g, h, i show the spatial patterns of surface
The temporal and spatial patterns of changes in
In the morning when ventilation is relatively weak (shallow PBL and weak sea
breeze), changes in
At night, changes in
In this section, we discuss changes in total and speciated PM
Figure 8 illustrates diurnal changes in total and speciated PM
Diurnal cycles for spatially averaged PM
During morning hours, averaged hourly total PM
Figure 9 presents spatial patterns of changes in total and speciated
PM
Spatial patterns in differences (present-day
In the morning, changes in total PM
A modified version of Fig. 9 that includes values for nonurban cells is in Supplement Fig. S18.
During the day, changes in speciated PM
At night, decreases in PM
In this study, we have characterized the impact of land surface changes via urbanization on regional meteorology and air quality in Southern California using an enhanced version of WRF–UCM–Chem. We use satellite data for the characterization of land surface properties and include a Southern California-specific irrigation parameterization. The two main simulations of focus in this study are a real-world present-day scenario and a hypothetical nonurban scenario; the former assumes current land cover distributions and irrigation of vegetative areas, while the latter assumes land cover distributions prior to widespread urbanization and no irrigation. We assume identical anthropogenic emissions in these two simulations to allow focus on the effects of land cover change on air pollutant concentrations.
Our results indicate that land surface modifications from historical
urbanization have had a profound influence on regional meteorology.
Urbanization has led to daytime reductions in air temperature for the lowest
model layer and reductions in ventilation within urban areas. The impact of
urbanization at nighttime shows the opposite effect, with air temperatures
and ventilation coefficients increasing. Spatially averaged reductions in air
temperature and ventilation during the day are
Changes in regional meteorology in turn affect concentrations of gaseous and
particulate pollutants.
This study highlights the role that land cover properties can have on
regional meteorology and air quality. We find that increases in
evapotranspiration, thermal inertia, and surface roughness due to historical
urbanization are the main drivers of regional meteorology and air quality
changes in Southern California. During the day, our simulations suggest that
increases in evapotranspiration and thermal inertia from urbanization lead
to regional air temperature reductions. Temperature reductions together with
increases in surface roughness contribute to decreases in ventilation and
consequent increases in ozone and PM
The Weather Research and Forecasting Model can be
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The supplement related to this article is available online at:
GABW designed the study. YL performed the model simulations, carried out data analysis, and wrote the paper. GABW and DJS mentored YL. JZ contributed to the setup of WRF–UCM–Chem. All authors contributed to editing the paper.
The authors declare that they have no conflict of interest.
This research is supported by the US National Science Foundation under grants
CBET-1512429, CBET-1623948, and CBET-1752522. Model simulations for the
work described in this paper are supported by the University of Southern
California's Center for High-Performance Computing
(
This paper was edited by Robert Harley and reviewed by three anonymous referees.