Geoinformation for Disaster and Risk Management - ISPRS

Improvements in model performance were obtained
by replacing surface parameters in the baseline dust
model. A series of model runs was executed using
combinations of satellite surface measurements.
Three parameters in particular, dust sources, digital
elevation, and surface roughness, led to substantial
improvements. These three parameters, along with
surface air temperature, wind speed, and wind
direction seem best for modeling dust entrainment.
For the PM10 fraction, all runs showed improvement
over the baseline run. For the PM2.5 fraction, results
were mixed, but were better than the baseline.
Performance statistics were defined for modeled and
observed atmospheric parameters (Yin et al., 2005),
and an “agreement index” was defined to assess
model performance (Table 1). Modeled results
agreed closely with observed measures for wind
speed and wind direction, but were statistically
different for surface air temperature. Results show
that the model system can simulate wind speed and
direction accurately; and, that surface air
temperature largely determines dust entrainment
potential. Figure 3 shows three evolutionary stages
in modeled dust patterns for the same storm.
Verification and validation
Verification and validation of modeled dust patterns
and movements were produced for hour of peak dust
concentration and dust episode duration using timestamped
ground observations against satellite
measurements. The correlation between modeled
and observed dust concentrations shows a tendency
to over-predict the severity of events, primarily
because satellite observations of the lower
atmosphere measure a greater thickness of dust than
is recorded by ground monitors. In all cases the
timing and duration of dust events was forecasted
accurately.
48
A weather forecaster's approach also was used to
assess performance. Statistics for 346 observations
were calculated. Only ten air quality exceedances
occurred over the entire model domain during these
events, suggesting that there were few false alarms.
When this method was applied to the Phoenix
metropolitan area, the model forecasted 71% of the
hourly averages, 29% of the hours exceeding air
quality standards for dust, and correctly forecasted
66% of the dust events.
Health tracking and surveillance
There are two basic approaches for health
information systems. One is based on medical
reporting by primary care givers; the other is based
on electronic information gathering through data of
historical reports and medical records. Both rely on
syndromes that detect outbreaks of illnesses or
potential epidemics that might otherwise be missed.
The approach in this report adopted an Internetbased
syndrome reporting system that facilitates
rapid communication between public health officials
in local jurisdictions and health care providers
(Budge et al., 2006). It is also a reporting and
discovery system for primary care physicians and
clinicians who want to determine if their patient's
syndrome has been reported by others in their
jurisdiction. It provides medical and environmental
information in three modules: (a) a syndrome
information collection module to which doctors
submit an inquiry; (b) a communication module
whereby public health officials respond to an
inquiry; and (c), a data visualization module that
permits both parties to review inputs in the medical
and geographical domains.
Experience with clinician-driven surveillance
systems demonstrates that health professionals will
report cases of suspected infectious disease, if the
system is fast (less than 15 30 seconds), provides
immediate feedback to clinicians on local infectious
disease outbreaks, permits selective interaction
between public health officials and clinicians on a
real-time basis, and is inexpensive. When clinicians
see a seriously ill patient with presumed infectious
disease, it should take only a few seconds to report
that case. Studies suggest that this is less than 0.1
percent of all clinical encounters in human medicine,
but these are the cases that need to be identified in
near real-time to avoid possible epidemics.
Products that fit user needs
Two types of health needs are met by tracking dust
events: those related to interventions that reduce
adverse respiratory effects in individuals; and, those
involving statistical relations between
environmental causes and public health effects. The
first use is for alerts issued by school nurses, print
and broadcast media, hospitals, doctors, and
clinicians who inform the public; the second is for
epidemiologists.