Long-term exposure to ambient ozone (O3) is
associated with a variety of impacts, including adverse human-health effects
and reduced yields in commercial crops. Ground-level O3 concentrations
for assessments are typically predicted using chemical transport models;
however such methods often feature biases that can influence impact
estimates. Here, we develop and apply artificial neural networks to
empirically model long-term O3 exposure over the continental United
States from 2000 to 2015, and we generate a measurement-based assessment of
impacts on human-health and crop yields. Notably, we found that two
commonly used human-health averaging metrics, based on separate
epidemiological studies, differ in their trends over the study period. The
population-weighted, April–September average of the daily 1 h maximum
concentration peaked in 2002 at 55.9 ppb and decreased by 0.43 [95 % CI:
0.28, 0.57] ppb yr-1 between 2000 and 2015, yielding an ∼18 %
decrease in normalized human-health impacts. In contrast, there was little
change in the population-weighted, annual average of the maximum daily
8 h average concentration between 2000 and 2015, which resulted in a
∼5 % increase in normalized human-health impacts. In both
cases, an aging population structure played a substantial role in modulating
these trends. Trends of all agriculture-weighted crop-loss metrics indicated
yield improvements, with reductions in the estimated national relative yield
loss ranging from 1.7 % to 1.9 % for maize, 5.1 % to 7.1 % for soybeans, and
2.7 % for wheat. Overall, these results provide a measurement-based
estimate of long-term O3 exposure over the United States, quantify the
historical trends of such exposure, and illustrate how different conclusions
regarding historical impacts can be made through the use of varying metrics.
Introduction
Tropospheric ozone (O3) is a secondary pollutant that is
photochemically formed from precursor gases. Exposure to ambient O3 is
associated with adverse health effects in humans (U.S. EPA, 2013) and reduced
yields in commercial crops (Chameides et al., 1994; Mauzerall and Wang,
2001). These impacts have driven efforts to reduce ground-level O3 in
the United States, specifically targeting peak levels of O3
concentrations through regulations that control anthropogenic precursor
emissions, such as nitrogen oxides (NOx) and volatile organic compounds
(VOCs). O3 reduction efforts have been widely successful in reducing
peak concentrations (Simon et al., 2015; Lefohn et al., 2017; Fleming et
al., 2018), but impacts related to both human health and crop yields
nonetheless persist (Cohen et al., 2017; Seltzer et al., 2018; Zhang et al.,
2018; Shindell et al., 2019).
Quantifying impacts requires an estimate of exposure to O3, which is
most commonly accomplished through the use of chemical transport models
(CTMs; e.g., Anenberg et al. 2010; Silva et al., 2013; Lelieveld et al., 2015;
Malley et al., 2017; Shindell et al., 2018; Stanaway et al., 2018). CTMs apply
state-of-the-science knowledge to simulate O3 formation, termination, and
transport, while also providing complete spatial and temporal coverage over
a particular domain – a desired trait for impact assessments. However,
estimates of exposure and impacts can vary substantially across CTM studies.
For example, two CTM-based studies estimated 2005 respiratory-related
premature mortalities in the USA using the same relative risk function
(Jerrett et al., 2009), yet yielded results that differed by ∼3× (i.e., 13 000 vs. 38 000; Zhang et al., 2018; Lelieveld et al., 2013).
While CTMs accurately reproduce many features of atmospheric chemistry
(Shindell et al., 2013; Hu et al., 2018), one important issue associated
with CTM-based impact assessments is that CTMs are consistently biased high
when predicting O3 concentrations (e.g., Schnell et al., 2015; Travis et
al., 2016; Yan et al., 2016; Seltzer et al., 2017, Porter et al., 2017; Guo
et al., 2018). Such biases can influence estimates of impacts and are often
amplified by nonlinear concentration–response functions (Seltzer et al.,
2018). Measurement-based methods, including area-weighted average of nearby
monitors, nearest monitor, inverse distance weighting, Kriging
interpolation, and multiple-linear regression under a Bayesian framework,
can also be used to estimate exposure (Bell, 2006; Brauer et al., 2008;
Marshall et al., 2008; Chang et al., 2010; Seltzer et al., 2018). However, a
notable limitation of such methods stems from the sparse spatial coverage of
monitoring sites. While these limitations might be minor in areas with dense
monitoring, such methods can become insufficient as the distance from
monitors increases (Bell, 2006).
O3 exposure trends are also of great interest to researchers and air
quality managers. To accurately model trends of O3 exposure, many
dimensions of variability must be captured. For the annual average of the
maximum daily 8 h average O3 concentration (hereafter MDA8), a
metric that has been used to quantify cause-specific long-term O3
exposure associations in epidemiological studies (e.g., Turner et al., 2016;
Lim et al., 2019), the O3 diurnal and seasonal cycles must be
accurately simulated over time. CTM evaluation studies also report the
existence of seasonal, spatial, and diurnal variability in model performance
(Cooper et al., 2014; Schnell et al., 2015; Seltzer et al., 2017; Lin et
al., 2017; Guo et al., 2018; Strode et al., 2019; Young et al., 2018), which
can lead to conflicting conclusions regarding trends in exposure. For
example, Zhang et al. (2018) report a ∼9 % decrease in the
population-weighted, daily maximum 1 h exposure concentration of O3
in the US warm season between 1990 and 2010. Meanwhile, a separate study
reported no change in the population-weighted, daily maximum 8 h exposure
concentration of warm-season O3 over those same 2 decades (Stanaway
et al., 2018). Since monitoring data are sparse, quantification of trends
using observations requires either continuous, long-term measurement data at
a particular site or the aggregation of observations into regions (e.g.,
Southeast, Northeast, Great Plains) and/or urban–rural–suburban
classifications. Many studies have indeed made use of such data to assess
O3 trends (Jaffe and Ray, 2007; Cooper et al., 2012, 2014; Parrish et al.,
2012; Simon et al., 2015). The recent publication of
the Tropospheric Ozone Assessment Report (TOAR) database (Schultz et al.,
2017) has created a rich observational dataset and further expanded the
number of such assessments (e.g., Chang et al., 2017; Gaudel et al., 2018;
Lefohn et al., 2018; Fleming et al., 2018; Mills et al., 2018b).
In this study, we applied artificial neural networks (ANNs) and the TOAR
database to estimate a suite of O3 impact metrics related to
human health and crop yield over the contiguous United States from 2000 to 2015
at 0.5∘×0.5∘ resolution. Specifically, we took
advantage of the improved long-term coverage afforded by the TOAR database
to develop a framework that empirically estimates O3 exposure with
complete spatial and temporal coverage over the United States. ANNs have
been previously used to make O3 predictions (Ruiz-Suárez et al.,
1995; Yi and Prybutok, 1996; Comrie, 1997; Gardner and Dorling, 2000; Dutot
et al., 2007; Di et al., 2017), but generally at the monitor or city level.
Our main goal was to better quantify the magnitude and trends of
population-weighted and agriculture-weighted long-term (i.e., months, annual)
O3 exposure in the USA over many consecutive years and use those
estimates to generate a measurement-based assessment of impacts and trends
on human health and crop yields. In addition, we tested and applied the ANN
to meteorologically adjust exposure predictions, thus eliminating a
substantial proportion of the short-term variability and enabling a separate
quantification of long-term O3 exposure trends.
MethodsObservational dataset and impact metrics
Daily O3 observations spanning 2000–2015 from the University of New
Hampshire Air Quality and Climate Program (Airmap), the U.S. Air Quality
System (AQS), the Canadian Air and Precipitation Monitoring Network
(CAPMoN), the U.S. Clean Air Status and Trends Network (CASTNET), the Global
Atmosphere Watch (GAW), and the Canada National Air Pollution Surveillance
(NAPS) monitoring networks in North America were retrieved from the
Tropospheric Ozone Assessment Report (TOAR) database (Schultz et al., 2017).
The reader is referred to Schultz et al. (2017) for a detailed description of
these networks, including variations in network area type (i.e., urban vs.
suburban vs. rural) and number of monitors. These daily observations were
used to calculate two human-health- and two crop-yield-relevant averaging
metrics. The first human-health metric is from the Jerrett et al. (2009),
hereafter J2009, long-term O3 exposure epidemiology study. Using data
from the American Cancer Society Cancer Prevention Study II (ACS CPS-II)
cohort, J2009 estimated changes in cause-specific mortalities attributable
to incremental changes in the April–September average of the daily 1 h
maximum O3 concentration (hereafter MDA1). The second human-health
metric is from the Turner et al. (2016), hereafter T2016, long-term O3
exposure epidemiology study. T2016, using an expanded version of the ACS
CPS-II cohort that included more follow-up years, a larger population, and
more events (i.e., deaths), reported changes in cause-specific mortalities
attributable to incremental changes in the annual average of the maximum
daily 8 h average O3 concentration (hereafter MDA8). To elucidate
the influence of the underlying seasonal trends on the MDA8 metric, we also
subdivided this annual metric into 3-month seasonal windows (i.e., summer:
June–August; spring: March–May). These seasonal divisions feature the
following labels: MDA8-MAM (spring), MDA8-JJA (summer), MDA8-SON (fall), and
MDA8-DJF (winter).
The two crop-loss metrics included here were the M12 (12 h mean) and AOT40
(accumulated amount of O3 over the 40 ppb threshold) averaging metrics.
Both have been used in a variety of crop loss assessments (e.g., Van Dingenen
et al., 2009; Avnery et al., 2011; Shindell et al., 2019). The M12 metric,
which can be used to calculate impacts on maize and soybean relative yields,
is defined as the mean O3 value for the local hours of 08:00–20:00,
averaged over the 3 months prior to the start of the harvest period. The
AOT40 metric, which can be used to calculate impacts on maize, soybeans, and
wheat, is an accumulative index and defined as a summation of the hourly
mean O3 values over 40 ppb for the local hours of 08:00–20:00, also
averaged over the 3 months prior to the start of the harvest period. We
initialized the start of the harvest period to be consistent with Avnery et
al. (2011). For maize and soybeans, the 3-month averaging period was
initialized in July. Wheat features two varieties with separate
initialization months for harvesting. One is initialized in March and the
other is initialized in May. Exposure results of both varieties are included
for illustrative and seasonal comparisons. It should be noted that long-term
O3 exposure also stunts the yields of a variety of other crops, such as
rice (Mills et al., 2007; Van Dingenen et al., 2009; Shindell et al., 2019),
but inclusion of these impacts was not considered here since they are not
major commercial crops in the United States.
Artificial neural network
We utilized feed-forward artificial neural networks (ANNs), which are also
referred to as multilayer perceptrons, to model the four metrics considered
here, with a unique network for each metric. ANNs were constructed using the
Keras API (https://keras.io, last access: 5 February 2020; Chollet, 2015) and TensorFlow machine-learning library
(https://www.tensorflow.org, last access: 5 February 2020; Abadi et al., 2015). Broadly, ANNs consist of several
interconnected layers, beginning with an input data layer, ending with an
output data layer, and having at least one “hidden” layer between the input
and output that models the nonlinear relationships of the system. Each layer
is connected via a set of coefficients at individual “nodes” that are
optimized through model training, similar to a multiple linear regression
(MLR) model. In contrast to a MLR, a layer in an ANN may have multiple
nodes, and the output from each node proceeds through an “activation
function”. An ANN activation function can take many shapes, but the two most
common are a sigmoidal function (which converts the node output to a
probability) and a rectified linear (ReLu) function (which applies a
threshold to a linear function). The ANNs used here consisted of one input,
three hidden, and one output layer. All nodes in each hidden layer featured
a ReLu activation function, including the output layer to ensure all
predictions were non-negative. The three hidden layers, each of which
included a bias term, consisted of 32 nodes each. This particular
architecture was selected following the testing of various configurations
(i.e., differences in the number of nodes and layers), with the ultimate goal to
prevent over-fitting of model parameters and maximizing model
generalization (see Sect. 3.1 for added discussion).
Variable input parameters (i.e., non-fixed effect parameters) for
each artificial neural network (ANN). All 24 h and 12 h (08:00–20:00)
periods were adjusted to local times.
ParameterAveragingUnitsSourceCloud area fraction24 h%MERRA-2; Gelaro et al. (2017)2 m air temperature12 hK10 m eastward wind speed24 hm s-110 m northward wind speed24 hm s-1Planetary boundary layer height12 hmTotal precipitation flux24 hkg m-2 d-1Sea-level pressure24 hPa2 m specific humidity12 hkg kg-1Leaf area index24 h%Surface shortwave radiation flux12 hW m-2Local anthropogenic NOx24 ht d-1CEDS; Hoesly et al. (2018)Local anthropogenic NMVOCLocal anthropogenic COEast Asian anthropogenic NOxmonthlyt per monthCEDS; Hoesly et al. (2018)East Asian anthropogenic COMEIC; Zheng et al. (2018)Methane concentrationsmonthlyppbvGLOBALVIEW-CH4 (2009)
Daily observations from the TOAR dataset spanning 2000–2015 were paired with
MERRA-2 meteorological reanalysis data (Gelaro et al., 2017), anthropogenic
emissions data from the Community Emissions Data System (CEDS) inventory
(Hoesly et al., 2018), monthly East Asian anthropogenic emissions (Hoesly et
al., 2018, Zheng et al., 2018), and monthly methane concentrations
(GLOBALVIEW-CH4, 2009). Details regarding these parameters are provided in
Table 1. Meteorological variables that were considered O3 covariates
largely follow Li et al. (2019). Local anthropogenic emissions (Hoesly et
al., 2018) included nitrogen oxides (NOx), non-methane volatile organic
carbon (NMVOC; includes total weight of all species), and carbon monoxide
(CO). Since emissions from East Asia have a large impact on North American
ground-level O3 concentrations (Liang et al., 2018) and have
dramatically changed in recent decades (Zheng et al., 2018), monthly total
emissions from all East Asian countries (i.e., China, Japan, South Korea,
North Korea, and Mongolia) were included as an input. Emissions from all
East Asian countries were retrieved from the CEDS inventory, with the
exception of Chinese emissions, which were retrieved from the
Multi-resolution Emission Inventory for China (MEIC) inventory (Zheng et
al., 2018). As the last year included in the CEDS inventory is 2014,
anthropogenic emissions for 2014 were repeated for 2015. To incorporate
geographical differences and long-term drivers not included as input,
several fixed-effect parameters were also used as input, including latitude,
longitude, and year. A single input into each ANN consists of all the
variables described above, which are paired in space and time to an
observation retrieved from the TOAR database. Finally, all input data were
normalized by subtracting the mean and dividing by the standard deviation of
the training dataset.
Prior to model training, the complete dataset was divided into three
components – training, validation, and testing. The training dataset is
used to iteratively tune the coefficients in the ANN, the validation dataset
is used to ensure the training process does not over-fit the ANN parameters
to match the training dataset, and the testing dataset is used to evaluate
how well the trained model performs. To compile these components, all
available data in a given month were collected and 3 random, consecutive
days were removed for validation, and 4 random, consecutive days were
removed for testing. The remaining days became the training dataset.
Overall, the size of the training dataset (i.e., the number of compiled
inputs) eclipsed 5 million values; therefore, the number of trainable
parameters was nearly 4 orders of magnitude smaller. The optimization of all
coefficients at each node in the ANN is accomplished through stochastic
gradient descent (SGD) optimization. SGD consists of (a) taking mini-batches
of the training dataset, (b) estimating the gradient of all coefficients
relative to the known output, (c) taking a small iterative step towards an
optimal solution, (d) repeating with a new mini-batch of the training
dataset, and (e) repeating steps (a)–(d) until the entire training dataset
has been fed through the network. Proceeding through steps (a)-(e) is
referred to as an epoch, and network training proceeds through multiple
epochs. In total, we used the Adam optimizer (Kingma and Ba, 2015), with a
learning rate of 0.001 (i.e., the size of step (c)), a decay factor of 0.9
(i.e., a shrinking of the step (c) size), and a mean-squared error target
cost-function. Each ANN was trained for 3000 epochs, with a shuffling of
the training data between each epoch. Through monitoring of the model
training using the validation dataset, it was determined that 3000 epochs
were sufficient to optimize the system without over-fitting.
To quantify the added benefit of the ANN over a simplified model, a
comparison with results from a MLR is included. In addition, since exposure
mostly occurs at unobserved locations, and all of the model training
explained thus far is only evaluated at observed locations (i.e., from the
TOAR database), we added an additional step to test our methods. In short,
we performed several CTM simulations and sampled the daily-level CTM
predictions of each metric at all available monitoring locations, generating
what we refer to as a “pseudo-observational dataset”. We then followed the
same machine learning process described above, except using the
pseudo-observational dataset and four newly trained ANNs, to predict the
population-weighted (MDA1/MDA8) and agriculture-weighted (M12/AOT40)
exposure values estimated by the CTM. Through this process, we can assess
the network's ability to predict total exposure through the exclusive use of
sparse measurements.
Chemical-transport modeling
GEOS-Chem was used to generate ground-level CTM predictions of O3
(v11-01; http://www.geos-chem.org, last access: 5 February 2020; Bey et al., 2001). A nested version of
the model at 0.5∘×0.625∘ horizontal
resolution, driven by native-resolution MERRA-2 meteorology and fed varying
2.0∘×2.5∘ boundary conditions, was
utilized to simulate O3 throughout the continental United States for
the years 2000, 2003, 2005, 2007, 2010, 2012, and 2014. The model includes
comprehensive HOx-NOx-VOC-Ox gas chemistry, coupled to an
aerosol module that includes sulfate–nitrate–ammonium chemistry (Park et
al., 2004; Pye et al., 2009), primary carbonaceous aerosols (Park et al.,
2003), mineral dust (Fairlie et al., 2007), and sea salt (Jaegle et al.,
2011), with aerosol thermodynamics simulated using ISORROPIA II (Fountoukis
and Nenes, 2007). Global anthropogenic emissions come from the CEDS
inventory (Hoesly et al., 2018) and were processed through the Harvard–NASA
Emission Component (HEMCO; Keller et al., 2014). All nested simulations
featured a 2-month spin-up, each O3 metric was calculated at local
time, and ground-level concentrations (10 m) from the first level of the
GEOS-Chem output were calculated using the methods described in Sect. 3 of
Zhang et al. (2012).
Calculation of metric trends
Trends of all metrics are presented both spatially and weighted towards the
subject of interest (i.e., population-weighted or agriculture-weighted).
Since the metrics considered here are based on long-term exposure, all
trends were assessed at the annual timescale (i.e., one data point, either
grid cell or a population-/agriculture-weighted value) using a linear
least-squares regression. To calculate population-weighted exposure
concentrations, we used population data from the 2017 revisions to the UN
Population Division (United Nations, 2017), distributed to
grid cells using population density data from the Gridded Population of the
World (GPW) version 4 (CIESIN, 2016). Agriculture-weighted exposure
concentrations were calculated using crop production and density data from the
Food and Agricultural Organization datasets (FAO, 2010).
We also accounted for short-term variability in metric trends by modeling
meteorologically adjusted predictions of each metric. To evaluate the ANNs'
ability to complete this task, we performed CTM simulations of 2003, 2005,
2007, 2010, 2012, and 2014 using meteorological conditions from each
respective year, but frozen anthropogenic emissions and methane
concentrations from 2000. We then used the previously trained ANNs (i.e., the
ANNs generated using the pseudo-observational data) to predict the
population-weighted and agriculture-weighted exposure metrics from these CTM
sensitivity simulations. Finally, we compared the CTM- and ANN-predicted
trends attributed to meteorology between 2000 and 2015. This enabled us to
evaluate how well the ANN can meteorologically adjust exposure trends. From
there, the same methods were applied using the ANNs trained with the TOAR
data to estimate meteorologically adjusted trends of the population-weighted
and agriculture-weighted exposure metrics. Specifically, all variables were
held frozen at 2000 values, except for the MERRA-2 meteorological
conditions.
Calculation of human-health and crop-yield impacts
Human-health impacts were quantified using the exposure–response
relationships and averaging metrics reported by J2009 and T2016. Both
epidemiological studies found a significant relationship between exposure to
long-term O3 and premature respiratory mortality. Respiratory impacts
are the lone end point considered here since they are the most common impact
reported by the community. However, T2016 and several other studies (Jerrett
et al., 2013; Crouse et al., 2015; Cakmak et al., 2016; Lim et al., 2019)
reported a significant relationship between long-term O3 exposure and
other mortality end points, such as cardiovascular disease.
Impact assessments for human health generally report results as the
estimated number of premature mortalities attributable to long-term
exposure. However, these results can often be driven by non-exposure
variables, such as changes in population count (Cohen et al., 2017),
baseline mortality rates (Cohen et al., 2017), and population aging (Apte et
al., 2018). To eliminate the influence of changes in the total population
count on net impacts, we normalized our results and report estimated health
impacts as premature mortalities per 100 000 people attributable to
long-term O3 exposure. We also illustrate the percent contributions of
each variable (i.e., population aging, changes in baseline mortality rates,
and exposure) on the net health impact calculations.
Normalized premature mortalities attributable to long-term O3 exposure
were calculated as follows.
1ΔX=0ifO3≤TMRELO3-TMRELifO3>TMREL2HR=expβΔY3AF=1-exp-βΔX4ΔMorti=y0i×AF×populationi5NormalizedMort=∑i=1nΔMorti/∑i=1npopulationi×100000
Here TMREL is the theoretical minimum risk exposure level, ΔX is
the predicted long-term O3 exposure concentration above the TMREL,
β is the exposure–response factor, HR is the hazard ratio reported by
the epidemiological study, ΔY is 10 ppb in both epidemiological
studies, AF is the attributable fraction of the disease burden attributable
to long-term O3 exposure, y0 is the cause-specific, age-binned
baseline mortality rate, population is the age-binned population count, i is
the age bin index, ΔMort is the estimated number of cause-specific,
age-binned premature mortalities, n is the number of age bins, and
Normalized Mort is the estimated number of cause-specific premature
mortalities per 100 000 people attributable to long-term O3 exposure.
Baseline mortality rates were retrieved from the 2017 GBD (Global Burden of
Disease) project (Stanaway et al., 2018) and mapped to best match the ICD-10
(International Statistical Classification of Diseases and Related Health
Problems) codes reported in T2016. The hazard ratio for respiratory diseases
was 1.040 (95 % CI: 1.013, 1.067) and 1.12 (95 % CI: 1.08, 1.16) in
J2009 and T2016, respectively. The TMRELs used were 33.3 ppb when using the
J2009 averaging metric and 26.7 ppb when using the T2016 averaging metric,
as reported by each epidemiological study.
We report agriculture (maize, soybean, and wheat) impacts in terms of a
national relative yield loss (RYL) due to long-term O3 exposure. We
utilized the concentration–response function and RYL methods outlined in Van
Dingenen et al. (2009), as summarized below.
6MaizeRYLM12=1-exp-M121242.83/exp-201242.837SoybeanRYLM12=1-exp-M121071.58/exp-201071.588MaizeRYLAOT40=AOT40×0.003569SoybeanRYLAOT40=AOT40×0.011310WheatRYLAOT40=AOT40×0.0163
Daily-level training, validation, and testing performance metrics
(RMSE) of the ANN using GEOS-Chem sampled data at TOAR locations compared to
a multiple linear regression model. Note ppbh denotes parts per billion-hour.
DatasetMDA1 MDA8 M12 AOT40 ANN (ppb)MLR (ppb)ANN (ppb)MLR (ppb)ANN (ppb)MLR (ppb)ANN (ppbh)MLR (ppbh)Training6.9110.696.6210.326.489.9362.8398.15Validation7.1610.606.9510.556.689.7464.9797.48Testing7.0910.506.8310.186.8610.0366.68100.43ResultsArtificial neural network training and evaluation
We began our evaluation using the pseudo-observational dataset derived from
daily GEOS-Chem output sampled at all available monitoring locations. From
there, we used this dataset to train four ANNs (i.e., one for each metric)
and attempted to recreate the original GEOS-Chem output. Through this
process, we attempted to determine the strength of an ANN in reconstructing
complete exposure maps using sparse observation data. The RMSE results from
the initial training and validation datasets were similar (Table 2),
indicating that the network was not over-fitting and generalized the system
well. When compared to a MLR, the RMSE testing results were ∼33 % lower, demonstrating the added benefit of the ANN (Table 2).
Population-weighted and agriculture-weighted exposure estimates from the ANN
closely matched the predictions from GEOS-Chem (red vs. blue in Fig. 1) for
all metrics and featured a high coefficient of correlation (insets in Fig. 1). An exception was the marginal high bias of the AOT40 metrics for wheat
early in the time series. These small deviations were due to a few factors. First,
regions of dense agriculture production are limited and generally located in
areas with fewer monitors, thus limiting the extent of model training.
Second, the AOT40 metric is an accumulation index, which can lead to the
amplification of small biases. Separately, we also found the ANN to perform
well when meteorologically adjusting the predicted exposure trends (i.e., the
short-term trends attributable to meteorology; see insets and green vs.
yellow lines in Fig. 1). In total, the ANN was able to reproduce the
complete exposure predictions with high fidelity, as estimated by GEOS-Chem,
using information strictly from monitoring locations.
Red – GEOS-Chem (GC) simulated values of all metrics. Blue – ANN
predictions of GC using daily samples from the GEOS-Chem simulations at all
available monitoring locations. Green – meteorological trend simulated by
GEOS-Chem with all input frozen at 2000 levels, with the exception of
meteorological variables. Yellow – ANN prediction of GC-MET (green) using
the previously trained (i.e., blue) neural networks. Inset within each panel
is the coefficient of correlation for the GC–ANN and GC-MET–ANN-MET time
series.
Daily-level training, validation, and testing performance metrics
(RMSE) of the ANN using TOAR observations compared to a multiple linear
regression model. Note that ppbh denotes parts per billion-hour.
With confidence in the overall framework, we then trained new ANNs using
daily 2000–2015 observations from the TOAR database. Little difference
between the training, validation, and testing performance metrics indicated
that each ANN was not over-fitting to the training dataset (Table 3). In
addition, we again found the ANN to perform ∼30 % better
than a MLR model (Table 3). When compared to the original TOAR database, we
found high accuracy between each ANN-predicted long-term metric and the
original observations (Figs. S1–S4; Table S1 in the Supplement). The RMSE of the MDA1 and MDA8
predictions ranged from 3.1 to 4.4 and 2.3 to 3.9 ppb, respectively
(Table S1). The r2 of the two metrics ranged from 0.77 to 0.84 and 0.74 to 0.82. Similar levels of bias (RMSE) and correlation
(r2) were found when comparing the long-term agriculture metrics (Table S1).
Trends of the MDA1 (a; ppb yr-1) and MDA8 (b;
ppb yr-1) health metrics from 2000 to 2015. Trends of MDA8-JJA
(c; ppb yr-1) and MDA8-DJF (d; ppb yr-1) from 2000 to 2015. The p values
from these trends can be found in Fig. S5.
Magnitude and trends of long-term O3 exposure metricsHuman-health-relevant metrics
The MDA1 metric featured large reductions throughout the study period, with
downward trends exceeding 1 ppb yr-1 in the Southeast and in portions of
California (Fig. 2). As a result, exposure throughout this period
simultaneously decreased. The national population-weighted exposure
concentration peaked in 2002 at 55.9 ppb, reached a minimum of 48.2 ppb in
2014, and featured sizable year-to-year fluctuations due to inter-annual
variation (Fig. 3). From 2000 to 2015, the national population-weighted
exposure concentration of the MDA1 metric featured an annual decrease of
0.43 [95 % CI: 0.28, 0.57] ppb yr-1 (Table S4). After adjusting for
meteorology, the trend changed to -0.41 [95 % CI: -0.35, -0.47] ppb yr-1.
The similar mean values of these two trends suggest that nearly all of the
MDA1 reductions are due to non-meteorological drivers (i.e., emission
changes, intercontinental transport, methane). Changes in exposure
featured an east–west divide, with population-weighted exposure
concentrations decreasing by 0.49 [95 % CI: 0.28, 0.69] ppb yr-1 in the
east and 0.31 [95 % CI: 0.21, 0.41] ppb yr-1 in the west (Fig. 3, Table S4).
(a) Population-weighted exposure concentrations of the MDA1
human-health metric from 2000 to 2015. The meteorologically adjusted trend is
in black with the slope in the inset. (b) The 2000–2015 population-weighted
trends (ppb yr-1) of the MDA1 (green) and MDA8 (red) metrics. The west–east
divide is made along the 95∘ W meridian and the whiskers span the 95 %
confidence interval. (c) Population-weighted exposure concentrations of
the MDA8 human-health metric from 2000 to 2015. The meteorologically adjusted
trend is in black with the slope in the inset. Tabulated values of these
plots can be found in Table S2 and S4.
In contrast, the MDA8 metric featured more modest decreases in the Southeast
USA and scattered areas with increasing trends (Fig. 2). This divergence
between the two human-health metrics is due to the different averaging
periods (i.e., the traditional “ozone season” vs. an annual average). If only
summer months were considered when calculating the MDA8 metric (i.e.,
MDA8-JJA), the two trends would be spatially and quantitatively consistent
(Fig. 2). However, O3 increases during the winter months (i.e.,
MDA8-DJF) partially compensated for the summer decreases, resulting in no
discernable trend for the national population-weighted MDA8 metric (Fig. 3).
After adjusting for meteorology, the national population-weighted MDA8 trend
from 2000 to 2015 is -0.02 [95 % CI: 0.01, -0.04] ppb yr-1 (Fig. 3). Similar to
the MDA1 metric, trends featured an east–west divide. It is interesting to
note that the western MDA8 trends were slightly positive and the eastern
MDA8 trends were slightly negative (Table S4). Separately, prior studies
(e.g., Bloomer et al., 2010; Cooper et al., 2012; Parrish et al., 2012;
Clifton et al., 2014; Simon et al., 2015; Strode et al., 2015; Fleming et
al., 2018) have highlighted the existence of a seasonal shift in the
distribution of O3 concentrations throughout the United States. We find that these shifts have not only manifested in
contrasting seasonal trends (i.e., summer decreases vs. winter increases),
but have also led to changes in the dominant months of O3 exposure. For
example, the population-weighted exposure concentrations during the spring
and summer months (MDA8-MAM vs. MDA8-JJA) were nearly equivalent from
2013 to 2015 (Table S2).
It should be noted that comparing previously reported seasonal trends of
O3 is difficult due to varying study periods, averaging metrics, and
selection of monitoring networks. Oftentimes, rural locations are
highlighted, enabling the isolation of trends in background O3
concentrations or the minimization of the influence of nearby changes in
anthropogenic emissions (e.g., Jaffe and Ray, 2007; Cooper et al., 2012;
Jaffe et al., 2018). These study-specific choices can effect conclusions.
For example, Simon et al. (2015) report that rural O3 monitors more
often feature statistically significant decreases in national mean MDA8
O3 during summer months and urban O3 monitors more often feature
statistically significant increases in national mean MDA8 O3 during
winter months. For this study, since our focus is on changes in exposure, we
incorporate all available observational data, including monitors in urban
cores. As such, when compared to prior studies, our conclusions regarding
O3 trends may be different.
Cooper et al. (2012), using rural monitoring data spanning 1990–2010,
reported a -0.45 and a +0.10 ppb yr-1 trend in daytime O3 during
summer months for the eastern and western USA, respectively. However, both
trends featured wide ranges. Jaffe et al. (2018), using a limited number of
high-elevation, rural monitoring sites, reported decreasing trends of median
summertime O3 between 2000 and 2016 at most analyzed locations, with
stronger decreases in the east than west (∼1 ppb yr-1 vs.
∼0.5 ppbẏr). Lin et al. (2017) also used rural monitoring
data, but increased the coverage to include 1988–2014 and found a 0.4–0.8 ppb yr-1 decreasing trend of median MDA8-JJA concentrations in the eastern
USA and mixed trends in the west. Fleming et al. (2018) incorporated both
urban and nonurban monitors and showed that the observed magnitude of
several warm season human-health ozone metrics is similar for North
American urban and nonurban sites and that the trends are only slightly
smaller for the urban areas. Broadly, we also found a dramatic divide
between east and west summertime O3 exposure trends, but our results
did feature some differences from prior studies. For example, our exposure-focused (i.e., population-weighted) estimates of eastern USA trends are similar to the mean reported by
Cooper et al. (2012) and on the low end of that reported by Lin et al. (2017). We also
found a consistent decreasing trend in western MDA8-JJA exposure, as well as
smaller levels of trend uncertainty (Table S4).
Cooper et al. (2012) also reported a uniform east and west increase in rural
wintertime O3 concentrations of 0.12 ppb yr-1. However, the exclusive
selection of rural monitors precludes the extrapolation of those results to
estimate exposure trends. This is well illustrated by Simon et al. (2015),
who used an extensive network of 1998–2013 observations to show that there
was a strong rural–urban divide in mean winter O3 trends, with
increasing trends more prevalent in urban areas. Indeed, when compared to
Cooper et al. (2012), we found a much stronger trend increase in MDA8-DJF
exposure (Table S4). Our results indicate that the national trend in
MDA8-DJF exposure was +0.33 [95 % CI: 0.37, 0.28] ppb yr-1 (Table S4),
with a near-uniform increase in both the east and west.
Trends of the M12 (a; ppb yr-1) and AOT40 (b, c, d; ppmh yr-1) agriculture metrics from 2000 to 2015. Note: JAS: July–September; MAM: March–May; MJJ: May–July. The p values from these
trends can be found in Fig. S6.
Crop-loss-relevant metrics
Since the averaging months for crop-loss metrics are dependent on crop
variety, the magnitude and trends can feature distinct patterns (Table S3).
All maize and soybean metrics envelop the months of July–September. As such,
and consistent with the MDA1 results, this averaging period yielded
widespread decreases for both the M12 and AOT40 metrics, with the strongest
reductions in the southeast and California (Fig. 4a, b). However,
both of these crops are predominantly grown in the Midwest and Great Plains
(Fig. S7). These regions generally experienced smaller trend reductions.
Nationally, the agriculture-weighted trends of the M12 metric for maize and
soybeans were -0.35 [95 % CI: -0.17, -0.54] ppb yr-1 and -0.39 [95 % CI:
-0.19, -0.59] ppb yr-1 (Fig. 5; Table S4). The agriculture-weighted trends of
the AOT40 metric for maize and soybeans were -0.35 [95 % CI: -0.18, -0.51] ppmh yr-1 and -0.39 [95 % CI: -0.21, -0.56] ppmh yr-1 (Fig. 5; Table S4).
After adjusting for meteorology, the mean trend for both metric and crop
pairings was reduced marginally, suggesting that meteorological factors
played a small role in the net trends from 2000 to 2015 (Fig. 5b).
(a) Agriculture-weighted exposure concentrations of the M12
agriculture metrics from 2000 to 2015. The meteorologically adjusted trend of
each metric is in black with the slope in the inset. (b) The 2000–2015
agriculture-weighted trends (ppb yr-1 for M12, ppmh yr-1 for AOT40) of the M12
and AOT40 metrics. The whiskers span the 95 % confidence interval. (c) Agriculture-weighted exposure concentrations of the AOT40 agriculture
metrics from 2000 to 2015 (trends of AOT40 soybean and AOT40 maize are nearly
equivalent). The meteorologically adjusted trend of each metric is in black
with the slope in the inset. Note: variable averaging periods are
considered, reflecting differences in crop harvest seasons. Tabulated values
of the left and right plots can be found in Tables S3 and S4.
Both agriculture-weighted AOT40 averaging periods for wheat (MAM and MJJ)
featured decreasing, but considerably different, trends (Fig. 5, bottom
panels). These trend differences again highlight the seasonal shift in
O3 concentrations. From 2000 to 2006, the AOT40-MJJ wheat metric was
∼40 %–60 % higher than the AOT40-MAM wheat metric (Table S3).
However, by 2014, both metrics were nearly equal. The 40 ppb accumulation threshold applied in the AOT40 calculation
also amplifies this convergence. Towards the end of the study period, mean
daytime O3 concentrations in the Midwest and Great Plains had decreased
sufficiently for the two metrics to nearly intersect.
Annual trends in the daytime 2 m temperature and daytime
2 m specific humidity from 2000 to 2015.
We posit that the influence of meteorology on the agriculture-weighted
trends, as indicated by the marginal difference in the mean of the
meteorologically adjusted and non-adjusted trends, is primarily due to two
factors. Prior analysis has shown that two important meteorological
variables influencing O3 include temperature and humidity (Camalier et
al., 2007; Jacob and Winner, 2009). The temperature–O3 mechanism is a
function of increasing temperatures promoting peroxyacetyl nitrate
decomposition (leading to ozone increases near NOx source regions, but
decreases in remote areas; Doherty et al., 2013) and increases in isoprene
emissions. The humidity–O3 mechanism is a function of increasing water
vapor concentrations, promoting O3 chemical destruction. According to
the MERRA-2 reanalysis product, the Midwest and Great Plains regions
featured both decreasing trends in daytime 2 m temperature and
increasing trends in daytime 2 m specific humidity (Fig. 6). In
addition, acute O3 episodes are notably sensitive to particular
meteorological variables (Russell et al., 2016; Fix et al., 2018), such as
temperature, providing an environment where meteorological variability can
disproportionately influence the magnitude of AOT40 values.
(a) Annual estimates of normalized premature mortalities (number
per 100 000 people per year) attributable to long-term O3 exposure
using the MDA1 and MDA8 averaging metrics and exposure–response function
from J2009 and T2016, respectively. The shaded region reflects the confidence
interval reported in each underlying epidemiological study. (b) The 2000 vs.
2015 percent contributions of population aging, changing baseline mortality
rates, and long-term O3 exposure to net normalized premature
mortalities using the MDA1 and MDA8 averaging metrics and exposure–response
function from J2009 and T2016, respectively. Tabulated values of (a) can be found in Table S5.
Estimates of long-term O3 exposure impactsHuman health
Human-health impacts, reported as the estimated number of premature
respiratory mortalities attributable to long-term O3 exposure per
100 000 people, were strongly dependent on the choice of exposure–response
relationship (Fig. 7). First, the T2016 results reported nearly double the
estimated human-health impacts attributable to long-term O3 exposure.
For example, in 2010, the J2009 and T2016 estimated impacts were
∼5.4 [95 % CI: 1.8, 8.7] and ∼11.3 [95 %
CI: 7.9, 14.5] premature mortalities per 100 000 people, respectively.
Second, the diverging trends of the two exposure metrics (Fig. 3) are
reflected in the estimated impacts (Fig. 7). Between 2000 and 2015, the MDA1
population-weighted exposure concentration decreased from ∼53.7 to ∼48.3 ppb (Table S2). As a result, the estimated
human-health impacts using the J2009 parameters decreased from
∼6.0 [95 % CI: 2.0, 9.7] to ∼5.0 [95 % CI:
1.7, 8.0] premature mortalities per 100 000 people (Table S5). In contrast,
the MDA8 population-weighted exposure concentration decreased from
∼39.9 to ∼39.1 ppb, yet the impacts using
the T2016 parameters increased from ∼10.8 [95 % CI: 7.6,
13.8] to ∼11.3 [95 % CI: 7.9, 14.5] premature mortalities
per 100 000 people (Fig. 7 and Table S5). These differences in estimated
impacts are not only due to changes in exposure. Over this period, an aging
population structure promoted increased susceptibility to O3 impacts.
In addition, depending on the age bin, baseline mortality rates for
respiratory diseases either marginally decreased or remained approximately
stable.
While impacts due to changes in exposure for both metrics decreased between
these end points, albeit by different magnitudes (blue bars in Fig. 7b;
-25.5 % vs. -5.7 %), these other determinants played a strong role in
modulating the estimated impact trends (Fig. 7b). The net changes in 2015 vs.
2000 normalized human-health impacts using the J2009 and T2016
exposure–response relationships and averaging metrics were -17.8 % and
+4.7 %, respectively (black bars in Fig. 7b). In both calculations, an
aging population structure substantially eliminated much of the gains from
exposure decreases (+15.5 %). Changing baseline mortality rates were
more modest, decreasing both calculations by 4.7 %.
The differences in estimated human-health impacts when using the J2009 and
T2016 exposure–response relationship and averaging metrics reported here are
consistent with prior studies (Malley et al., 2017; Seltzer et al., 2018;
Shindell et al., 2018). That is, the estimated human-health impacts when
using the T2016 exposure–response relationship and averaging metric are
considerably higher than the results computed when using the J2009
parameters. However, to our knowledge, the historical evolving differences
between the two have yet to be shown. For example, the T2016 results were
∼80 % higher than the J2009 results in 2000 (Table S5). By
2008, the T2016 results were nearly double the J2009 results, and this
difference continued to grow over time (∼130 % in 2015).
Between 2000 and 2015, our net estimated premature mortalities attributable to
long-term O3 exposure in the USA ranged from ∼14500 to 19 200 when using the J2009 parameters and from ∼29800 to 37 600 when using the T2016 parameters. These results are lower than
analogous prior studies that are based solely on CTM estimates of O3
exposure. An exception is Zhang et al. (2018), who found comparable results
when using the J2009 epidemiological study. However, Zhang et al. (2018)
report a 13 % increase in premature mortalities attributable to long-term
O3 exposure in the United States between 1990 and 2010, despite O3
decreases. We find a ∼6.7 % decrease in premature
mortalities attributable to long-term O3 exposure, albeit over
2000–2015. This is likely due to the dramatic decreases in O3 precursor
emissions that occurred post-2000 (Xing et al., 2013; Simon et al., 2015).
Crop loss
Agriculture impacts for each of the crop varieties considered here decreased
from 2000 to 2015 (Fig. 8). When using the M12 metric, the estimated national
RYL values for maize and soybeans in 2000 were 4.6 % and 16.3 %, respectively
(Fig. 8 and Table S5). These values decreased to 2.9 % and 11.2 % in
2015. When using the AOT40 metric, the estimated national RYL values for maize,
soybeans, and wheat for the year 2000 were 3.4 %, 11.9 %, and 12.1 %,
respectively. By 2015, these RYL values dropped to 1.6 %, 4.8 %, and
9.4 %, respectively. Broadly, these estimated agriculture yield impacts
are comparable to the global “ozone yield gaps” (i.e., RYL) modeled by
Mills et al. (2018a), who considered the flux-based, stomatal uptake of
O3 for each crop.
Estimates of the national relative yield loss for a variety of
commercial crops using ANN-calculated exposure metrics. Tabulated values of
this plot can be found in Table S5.
Several other characteristics are consistent among all of the crop varieties
and metric combinations considered here. For one, estimated RYL featured
sizable inter-annual variability, indicating that the impacts calculated
from a single year might not be representative of a particular period. For
example, the RYL for soybeans, when using the AOT40 metric, increased from
7.8 % in 2004 to 11.6 % in 2005 – a nearly ∼50 %
increase. Second, similar to Van Dingenen et al. (2009) and Lapina et al. (2016), impacts were consistently higher when utilizing the M12 metric and
the associated concentration–response functions. These differences also
became amplified over time. The RYL for soybeans in 2000 using the M12
metric (16.3 %) was ∼37 % higher than the RYL using the
AOT40 metric (11.9 %). This difference increased to ∼135 % (11.2 % vs. 4.8 %) by 2015 (Table S5). These diverging trends
occur for two reasons. First, the daytime O3 concentrations approached
the AOT40 threshold of 40 ppb post-2007 (Table S3). This decrease
precipitated disproportional improvements in AOT40-calculated RYL. Second,
and to a lesser degree, the slopes of the two soybean concentration–response
functions are different (Fig. S8).
Uncertainties, limitations, and additional remarks
Studies quantifying the health impacts attributable to long-term PM2.5
exposure oftentimes use higher-resolution products (e.g.,
0.1∘×0.1∘) that harness satellite data
(e.g., Apte et al., 2015; Cohen et al., 2017; van Donkelaar et al., 2019).
However, a number of complications prevent such products for surface O3
(Duncan et al., 2014). Regardless, we believe this 0.5∘×0.5∘ product is of sufficient resolution to estimate
long-term O3 exposure for a number of reasons. First, O3 features
a residence time on the order of hours to days in the lower troposphere and
in urban environments (Parrish et al., 2012; Monks et al., 2015), providing
sufficient time for localized and regional mixing. Second, unlike short-term
O3 exposure, long-term O3 exposure is less sensitive to singular
events that are more heterogeneous in space and time. Third, regional CTM
studies report only marginal differences in O3 concentrations and
estimated impacts when scaling from 12 km to resolutions comparable to those in this
analysis (Punger and West, 2013; Gan et al., 2016).
In terms of impacts, there is evidence that O3 affects more than what
was presented here. For example, several epidemiological studies suggest
that human-health impacts may extend to cardiovascular mortality (Jerrett et
al., 2013; Crouse et al., 2015; Cakmak et al., 2016; Turner et al., 2016;
Lim et al., 2019). Separately, our analysis applied a log-linear
exposure–response function when performing the human-health calculations
since it is most common method applied in the community. There is evidence that this
relationship may instead be linear (Di et al., 2017). For agriculture, the
exposure–response functions utilized here are “pooled” from studies
featuring a limited number of cultivars grown in the USA and Europe (Van
Dingenen et al., 2009). While considered reliably representative of the
commonly grown cultivar population in these regions, extrapolation of these
relationships to the national level may introduce additional uncertainty. In
addition, the methodology selected here does not take into account changes
in plant conditions that may limit or exacerbate conditions which influence
the opening of stomata and the ability of a plant to uptake O3, such as
temperature and soil moisture. The results presented here also demonstrate
the need for additional epidemiological studies to test the utility of
common averaging metrics that are used when estimating health impacts.
Specifically, clarity is needed regarding whether long-term O3
health impacts are more sensitive to peak averaged (i.e., the MDA1 metric) or
annually averaged (i.e., the MDA8 metric) O3 exposure.
Finally, long-term trends of O3 are driven by a number of mechanisms,
including intercontinental transport (Fiore et al., 2009; Lin et al., 2012, 2017) and methane concentrations (Fiore et al., 2002; Shindell
et al., 2017; Lin et al., 2017). For example, Lin et al. (2015) conclude that
rising Asian emissions and global methane have played a key role in the
increase in western USA springtime O3 from 1995 to 2014. These drivers
merit additional study, with an emphasis on exploring seasonal differences
that influence impact metrics. Furthermore, inclusion of observations from the
most recent years (i.e., 2015–2017) should be targeted. Since Chinese
emissions of NOx peaked in 2012 (Zheng et al., 2018), our current and
future estimates of intercontinental transport influences on background
O3 might warrant revisiting.
Conclusions
Through the application of artificial neural networks, we empirically model
the magnitude and trends of long-term (i.e., seasonal, annual) ambient
O3 over the continental United States from 2000 to 2015. We then used
these estimates of long-term O3 exposure to generate a
measurement-based assessment of impacts on human health and crop yields. All
metrics with averaging periods spanning the traditional O3 season (i.e., warm months)
featured peak exposure in 2002, with net decreases over the course of the
study period. For example, the population-weighted, April–September average
of the daily 1 h maximum O3 concentration (i.e., MDA1 from Jerrett et
al., 2009) decreased by 0.43 [95 % CI: 0.28, 0.57] ppb yr-1 between
2000 and 2015. In contrast, there was little change in the population-weighted,
annual average of the maximum daily 8 h average O3 concentration
(i.e., MDA8 from Turner et al., 2016) between 2000 and 2015. There were
compensating seasonal effects, with wintertime O3 increases and
summertime O3 decreases, yielding a net population-weighted trend of
-0.03 [95 % CI: 0.04, -0.10] ppb yr-1. Human-health metric trends also
featured an east–west divide, with stronger decreases in the eastern USA.
All agriculture-weighted crop-loss metrics featured decreasing trends over
the study period.
Human-health impacts were quantified in terms of the estimated number of
premature respiratory mortalities attributable to long-term O3 exposure
per 100 000 people. Crop-loss impacts were quantified in terms of the
estimated national relative yield loss for a variety of commercial crops.
Normalized human-health impact estimates decreased by ∼18 % and increased by ∼5 % when using the Jerrett et al. (2009) and Turner et al. (2016) averaging metrics and parameters, respectively.
In both cases, exposure changes and an aging population structure played a
substantial role in modulating these trends. When using the M12 metric, the
relative yield loss (RYL) due to O3 exposure for maize and soybeans
improved by 1.7 % and 5.1 %. When using the AOT40 metric, the net
benefits were greater, with the RYL for maize, soybeans, and wheat improving
by 1.9 %, 7.1 %, and 2.7 %, respectively. These different responses
are mainly due to the daylight O3 concentrations approaching the 40 ppb
AOT40 threshold by the end of the study period. Overall, these results
provide a measurement-based estimate of long-term O3 exposure over the
United States, quantify the historical trends of such exposure, and
illustrate how different conclusions regarding historical impacts can be
made through the use of varying metrics.
Data availability
Maps of the O3 exposure metrics used in this study can be accessed by
contacting one of the corresponding authors.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-1757-2020-supplement.
Author contributions
All authors contributed to the design and/or methodology of the study.
KMS applied the methods, analyzed the results, developed all
figures/tables, and drafted the initial manuscript. All authors edited and
contributed to subsequent drafts of the manuscript.
Competing interests
The authors declare that they have no conflicts of interest.
Acknowledgements
Karl M. Seltzer was supported by NASA headquarters under the NASA Earth and Space
Science Fellowship Program – grant no. 80NSSC17K0354. All O3
observations were retrieved from the TOAR database via the Representational
State Transfer (REST) services. Special thanks to the TOAR team/community,
particularly Owen Cooper, Martin Schultz, and Sabine Schröder, for
compiling all of the observations into a variety of metrics; the MEIC team
for providing Chinese anthropogenic emissions; the Duke Compute Cluster for
computational resources; and the University of Florida's HiPerGator for
computational resources.
Financial support
This research has been supported by the NASA Earth and Space Science Fellowship Program (grant no. #80NSSC17K0354).
Review statement
This paper was edited by Pedro Jimenez-Guerrero and reviewed by three anonymous referees.
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