Radical precursors and related species from traffic as observed and modeled at an urban highway junction.

Nitrous acid (HONO) and formaldehyde (HCHO) are important precursors for radicals and are believed to favor ozone formation significantly. Traffic emission data for both compounds are scarce and mostly outdated. A better knowledge of today's HCHO and HONO emissions related to traffic is needed to refine air quality models. Here the authors report results from continuous ambient air measurements taken at a highway junction in Houston, Texas, from July 15 to October 15, 2009. The observational data were compared with emission estimates from currently available mobile emission models (MOBILE6; MOVES [MOtor Vehicle Emission Simulator]). Observations indicated a molar carbon monoxide (CO) versus nitrogen oxides (NO(x)) ratio of 6.01 +/- 0.15 (r2 = 0.91), which is in agreement with other field studies. Both MOBILE6 and MOVES overestimate this emission ratio by 92% and 24%, respectively. For HCHO/CO, an overall slope of 3.14 +/- 0.14 g HCHO/kg CO was observed. Whereas MOBILE6 largely underestimates this ratio by 77%, MOVES calculates somewhat higher HCHO/CO ratios (1.87) than MOBILE6, but is still significantly lower than the observed ratio. MOVES shows high HCHO/CO ratios during the early morning hours due to heavy-duty diesel off-network emissions. The differences of the modeled CO/NO(x) and HCHO/CO ratios are largely due to higher NO(x) and HCHO emissions in MOVES (30% and 57%, respectively, increased from MOBILE6 for 2009), as CO emissions were about the same in both models. The observed HONO/NO(x) emission ratio is around 0.017 +/- 0.0009 kg HONO/kg NO(x) which is twice as high as in MOVES. The observed NO2/NO(x) emission ratio is around 0.16 +/- 0.01 kg NO2/kg NO(x), which is a bit more than 50% higher than in MOVES. MOVES overestimates the CO/CO2 emission ratio by a factor of 3 compared with the observations, which is 0.0033 +/- 0.0002 kg CO/kg CO2. This as well as CO/NO(x) overestimation is coming from light-duty gasoline vehicles.

Estimation of biogenic emissions with satellite-derived land use and land cover data for air quality modeling of Houston-Galveston ozone nonattainment area.

The Houston-Galveston Area (HGA) is one of the most severe ozone non-attainment regions in the US. To study the effectiveness of controlling anthropogenic emissions to mitigate regional ozone nonattainment problems, it is necessary to utilize adequate datasets describing the environmental conditions that influence the photochemical reactivity of the ambient atmosphere. Compared to the anthropogenic emissions from point and mobile sources, there are large uncertainties in the locations and amounts of biogenic emissions. For regional air quality modeling applications, biogenic emissions are not directly measured but are usually estimated with meteorological data such as photo-synthetically active solar radiation, surface temperature, land type, and vegetation database. In this paper, we characterize these meteorological input parameters and two different land use land cover datasets available for HGA: the conventional biogenic vegetation/land use data and satellite-derived high-resolution land cover data. We describe the procedures used for the estimation of biogenic emissions with the satellite derived land cover data and leaf mass density information. Air quality model simulations were performed using both the original and the new biogenic emissions estimates. The results showed that there were considerable uncertainties in biogenic emissions inputs. Subsequently, ozone predictions were affected up to 10 ppb, but the magnitudes and locations of peak ozone varied each day depending on the upwind or downwind positions of the biogenic emission sources relative to the anthropogenic NOx and VOC sources. Although the assessment had limitations such as heterogeneity in the spatial resolutions, the study highlighted the significance of biogenic emissions uncertainty on air quality predictions. However, the study did not allow extrapolation of the directional changes in air quality corresponding to the changes in LULC because the two datasets were based on vastly different LULC category definitions and uncertainties in the vegetation distributions.