PM10 emissions from nonpoint sources need to be
quantified in order to effectively meet air quality standards.
In California's Central Valley, agricultural operations are
highly complex but significant sources of PM10 that are difficult
to quantify using point sampling arrays. A remote
sensing technique, light detection and ranging (lidar),
using a small field portable, fast-scanning lidar shows great
potential for measuring PM10 emissions from agricultural
nonpoint sources. The qualitative capabilities of the lidar
instrument are demonstrated for land preparation
operations at a wheat field. The range (>5 km), spatial
resolution (2.5 m) and fast response times (s) of the lidar
allow the following: (i) plume dynamics to be described in
detail and eventually to be modeled as a function of
source fluctuations and environmental conditions, (ii)
measurements of average wind speed and direction over 50−100 m scales, (iii) quantitative determination of the
fraction of dust missed by point sampling arrays, and (iv)
currently provide unparalleled information on nonpoint source
emission variability, both temporally and spatially. The
lidar data indicate the line source nature of plumes from
tractor operations and suggest that fast lidar 2D vertical
scans downwind of nonpoint sources will provide the
best PM10 emission factor measurements. Widespread
use of lidar for direct quantitative emission factor
measurement depends on careful determination of
particulate matter backscatter−mass calibration relationships.