We exploited publicly available satellite- and aircraft-based imagery to estimate urban vegetation cover fraction and land use by class for a semiarid urban area that includes Phoenix, AZ, USA, using low-cost and technologically modest tools. This technique is also used to evaluate two satellite-derived tree cover datasets as well as to compare estimates from the present study with land cover data generated from another study performed using the same study domain. The approach outlined in this article entails the use of Google Earth® images that are analyzed either visually or by using a more rigorous visually supervised digital reclassification method. Neither method is automated. Determination of optimal sample size was also an objective of the study. The limitations and advantages associated with these approaches are described. © 2012 Copyright Taylor and Francis Group, LLC.

Measurements of BVOC emissions, meteorological parameters, and solar radiation were carried out in a temperate forest, China during the summer seasons in 2010 and 2011. Terpenoid emissions were measured using the Relaxed Eddy Accumulation (REA) technique on an above–canopy tower. Isoprene contributed 79.1% and 82.0% of terpenoid emissions in 2010 and 2011 summer. The monoterpene emissions were dominated by α–pinene, contributing 6.3% and 12.2% of the total terpenoid emissions in 2010 and 2011 summer. Terpenoid emissions exhibited strong diurnal variations. Isoprene and monoterpene emissions maxima typically occurred a few hours after the noon PAR peak and coincided with the daily temperature maximum. During 2011 summer, the mean isoprene emission flux (mg m–2 h–1) was 0.889, mean total monoterpene emission flux was 0.143. Emission factors, representing the emission expected at a temperature of 30 °C, for this site were 0.32 mg m–2 h–1 for total monoterpenes and 4.3 mg m–2 h–1 for isoprene. The observations were used to evaluate the isoprene and monoterpene emission magnitude and variability predicted by the MEGANv2.1 model. Canopy scale isoprene and monoterpene emission factors based on these observations fall within the range of emission factors assigned to locations within 50 km of the site by the MEGANv2.1 emission model. When using the site specific landcover data for the site, the measured emission factors are 12% for isoprene and 20% for monoterpenes lower than the MEGANv2.1 emission factors. MEGANv2.1 predicts that variations in light intensity should result in significant changes in isoprene emissions during the study but this was not evident in the observations. Observed diurnal, seasonal and interannual variations in isoprene and monoterpene emissions were strongly correlated with air temperature which was the dominant driving variable for MEGANv2.1 during the study period. The observed temperature response for isoprene and monoterpenes is similar to the temperature sensitivity of the MEGANv2.1 response functions.

Biogenic volatile organic compounds (BVOC) contribute significantly to the formation of ozone and secondary organic aerosol (SOA). The Model of Emissions of Gases and Aerosols from Nature (MEGANv2.02) is used to estimate emissions of isoprene, monoterpenes (MT), and sesquiterpenes (SQT) across the United States. Compared to the Biogenic Emission Inventory System (BEIS3.0), MEGANv2.02 estimates higher isoprene but lower MT emissions for July 2001 and January 2002. A sensitivity study of SQT and MT emission factors and algorithm parameters was conducted by assigning values to four plant functional types (PFTs) using both recent measurements and literature values. The standard deviations of the emissions factors within these PFTs were two to four times the averages because of the variation in experimental basal emissions rate data. More recently published SQT and MT basal emission rates are generally lower than those reported in the literature through 2004. With the new emissions factors, monthly average SQT emission rates for the contiguous United States are equal to 16% of the MT emissions during July and 9% of the emissions during January. The SQT emissions distribution is strongly influenced by the grass and crop PFT, for which SQT emissions data are quite limited.

Biogenic volatile organic compound (BVOC) emissions were studied using vegetation enclosure experiments. Particular emphasis was given to sesquiterpene compounds (SQT), although monoterpenes (MT) were also characterized. SQT were detected in emissions from seven (out of eight) pine species that were examined. Thirteen SQT compounds were identified; the most abundant ones were beta-caryophyllene, alpha-bergamotene, beta-farnesene, and alpha-farnesene, with emission rates increasing exponentially with temperature. Regression analysis yielded exponential dependencies of both MT and SQT emissions on temperature of the form E = E0 x exp(beta(T - T0)). This resulted in SQT basal emission rates (E0 defined at T0 = 30 degrees C) ranging between <4 and 620 ng (carbon) gdw(-1) h(-1) (gdw = gram dry weight). The average value of the exponential temperature response factor beta for SQT emissions, taken from all experiments, was 0.17 degree C(-1), whereas the value for monoterpenes was 0.11 degrees C(-1). The average, total SQT emissions from pines were estimated to be 9, 16, and 29% of the MT emissions at 20, 30, and 40 degrees C respectively. The emission factors and beta-factors determined from these measurements were used to estimate pine tree MT and SQT emission distributions for the contiguous United States using MEGAN (model of emissions of gases and aerosols from nature, Guenther et al., 2006). SQT fluxes reaching 10-40 mg m(-2) for the month of July were estimated for extensive areas of most western and southern U.S. states.

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The impact of climate change on surface-level ozone is examined through a multiscale modeling effort that linked global and regional climate models to drive air quality model simulations. Results are quantified in terms of the relative response factor (RRF(E)), which estimates the relative change in peak ozone concentration for a given change in pollutant emissions (the subscript E is added to RRF to remind the reader that the RRF is due to emission changes only). A matrix of model simulations was conducted to examine the individual and combined effects offuture anthropogenic emissions, biogenic emissions, and climate on the RRF(E). For each member in the matrix of simulations the warmest and coolest summers were modeled for the present-day (1995-2004) and future (2045-2054) decades. A climate adjustment factor (CAF(C) or CAF(CB) when biogenic emissions are allowed to change with the future climate) was defined as the ratio of the average daily maximum 8-hr ozone simulated under a future climate to that simulated under the present-day climate, and a climate-adjusted RRF(EC) was calculated (RRF(EC) = RRF(E) x CAF(C)). In general, RRF(EC) > RRF(E), which suggests additional emission controls will be required to achieve the same reduction in ozone that would have been achieved in the absence of climate change. Changes in biogenic emissions generally have a smaller impact on the RRF(E) than does future climate change itself The direction of the biogenic effect appears closely linked to organic-nitrate chemistry and whether ozone formation is limited by volatile organic compounds (VOC) or oxides of nitrogen (NO(x) = NO + NO2). Regions that are generally NO(x) limited show a decrease in ozone and RRF(EC), while VOC-limited regions show an increase in ozone and RRF(EC). Comparing results to a previous study using different climate assumptions and models showed large variability in the CAF(CB).

Implications

We present a methodology for adjusting the RRF to account for the influence of climate change on ozone. The findings of this work suggest that in some geographic regions, climate change has the potential to negate decreases in surface ozone concentrations that would otherwise be achieved through ozone mitigation strategies. In regions of high biogenic VOC emissions relative to anthropogenic NO(x) emissions, the impact of climate change is somewhat reduced, while the opposite is true in regions of high anthropogenic NO(x) emissions relative to biogenic VOC emissions. Further, different future climate realizations are shown to impact ozone in different ways.