The effects of exchanging noncertified with certified wood-burning devices on the 24h-average PM2.5 concentrations in the nonattainment area of Fairbanks, Alaska, in a cold season (October to March) were investigated using the Weather Research and Forecasting model inline coupled with a chemistry package. Even changing out only 2930 uncertified woodstoves and 90 outdoor wood boilers reduced the 24 h-average PM2.5 concentrations on average by 0.6
In 2006, the Environmental Protection Agency (EPA) has tightened the 24 h National Ambient Air Quality Standards (NAAQS) to 35
In Fairbanks, wood-burning devices are major contributors to the PM2.5 emissions in residential areas [
The emissions from wood-burning devices vary with fuel type, fuel moisture, burning practice, and control techniques of the devices [
The effects of woodstove changeout programs on reducing ambient PM2.5 concentrations have been evaluated mainly based on observations. For example, the PM2.5 sampling campaign related to the changeout of 1200 uncertified woodstoves in Libby, Montana, showed that 24 h-average PM2.5 concentrations decreased by 20% during the changeout period [
Of the 8610 inserts and woodstoves in Fairbanks, about 2930 devices are uncertified ones [
Whereas the observational approach applied in mid-latitudes requires an extensive measurement campaign over the changeout program lifetime, numerical modeling can provide a quick and low-cost assessment of the benefits of a wood-burning device changeout program. Furthermore, modeling permits assessment of the potential benefits of a changeout program prior to its implementation/completion and hence permits implementation of additional measures in case the changeout program alone may not be sufficient enough to achieve compliance.
To this aspect, the Weather Research and Forecasting model inline coupled with a chemistry model commonly known as WRF/Chem [
Among many efforts in seeking effective pollution controls to comply with the NAAQS, Fairbanks started conducting a “woodstove replacement” program. Given that Fairbanks’ 2008 design value is 44.7
Simulations were performed for October 1, 2008 0000 UTC, to April 2, 2009 0000 UTC, with the Alaska modified WRF/Chem in forecast mode. The physical and chemicals schemes selected for the simulations are listed in Table
Parameterizations used in this study.
Process | Scheme and reference |
---|---|
Cloud microphysics | Six water-class cloud microphysical scheme [ |
Subgrid-scale convection | Further developed 3D version of the Grell-Dévényi cumulus-ensemble scheme [ |
Radiation | Goddard shortwave radiation scheme [ |
Atmospheric boundary layer and sublayer processes | [ |
Land-surface processes | Modified Rapid Update Cycle land-surface model [ |
Gas-phase chemistry | [ |
Photolysis frequencies | [ |
Aerosol physics, chemistry and dynamics | Modal Aerosol Dynamics Model for Europe [ |
Dry deposition | [ |
Biogenic emissions | calculated inline depending on meteorological conditions [ |
The model domain encompasses most of Interior Alaska centered over the Fairbanks nonattainment with 4 km horizontal grid-increment from the surface to 100 hPa with 28 stretched vertical layers (Figure
Average PM2.5 concentration in the domain of interest in October to March as obtained in REF with terrain contours overlain. The star and red polygon indicate the grid cell holding the official monitoring site and the outline of the nonattainment area.
We performed simulations without (REF) and with “woodstove replacement” (WSR). In WSR, the numbers of wood-burning devices to be changed out were based on [
We developed the annual anthropogenic emission inventory based on the National Emission Inventory (NEI) of 2008 available by October 2010. As no point-source emissions were available at that time, we used point-source emission data from facility operators (if provided) and assumed a 1.5%/y increase from the previous NEI otherwise. For some industrial/commercial/institutional sectors that were not available in the NEI2008, we assumed they remained as in the NEI2005 as there was just marginal change in these sectors over 2005–2008. Emission estimates for residential wood combustion were obtained from [
We considered changes in emission of PM2.5, particulate matter having diameters equal to or less than 10
This annual emission data was allocated in space and time based on source-specific activity data (land use, population density, traffic counts, point-source coordinates, hour, day of the week, month, etc.) (e.g., Figure
Zoom-in on PM2.5 emissions in (a) REF, (b) WSR, (c) WSS1, and (d) WSS2 on average over October to March for REF and WSR and October 01–14, 2008, for WSS1 and WSS2.
We analyzed the simulations over an area of 80 × 70 grid points (Figure
We evaluated the benefit of the wood-burning device changeout by examining how many “exceedances” and “exceedance days” were avoided. In doing so, we considered 24 h-average PM2.5 concentrations at any grid-cell greater than the NAAQS on any day as an “exceedance” and any day that had at least one “exceedance” anywhere as an “exceedance day”.
We calculated the relative response factors in response to the emission changes YYY by dividing the concentrations in YYY by those of REF (YYY/REF) where YYY stands for WSR, WSS1, and WSS2, respectively. The RRFs were calculated for total PM2.5 and its major components namely sulfates (SO4), nitrates (NO3), ammonium (NH4), organic carbon (OC), elemental carbon (EC), and other primary inorganic particulate matter (others). The RRFs were calculated for all grid cells in the nonattainment area including the grid cell that holds the official monitoring site to assess the effects of the wood-burning device changeout over the nonattainment area.
The evaluation of the baseline simulation (REF) [
The failure to capture the PM2.5 maxima (minima) to their full extent on extremely polluted (clean) days does not affect the number of simulated exceedance days and exceedances. During these events, PM2.5 concentrations namely were much higher (lower) than the 35
On annual average, PM2.5 emissions from residential heating devices made up about 21% of the total PM2.5 emissions from all source categories. Wood-burning devices contributed 66.6, 1.4, 14.7, 59.9, 96.5 and 95.8% of the emitted PM2.5, SO2, NO
On average over the nonattainment area, PM2.5 emissions in October, November, December, January, February, and March were 941.7, 632.9, 632.5, 799.8, 680.5, and 661.0 g.km-2 h−1, respectively. Temperatures were appreciably below the 1971–2000 30-year average in October and above in November, December, January, and February. Consequently, PM2.5 emissions were higher in October and lower in November, December, and January than on average.
Over October to March, WSR reduced the total PM2.5 emissions by 3.7% compared to REF. The monthly average PM2.5 emission reductions were 4.0, 3.2, 2.7, 3.0, 3.9, and 5.6% in October, November, December, January, February, and March, respectively. The magnitude of emission reductions differed among pollutants. On average over the nonattainment area, SO2 emission reductions were 19.5, 8.16, 9.1, 11.7, 11.0, and 15.8% in October to March, respectively. The respective NO
The diurnal courses of PM2.5 concentrations were similar in REF and WSR, that is, changes in emissions from wood burning do not affect the general diurnal course of PM2.5 concentration. The diurnal course of PM2.5 concentration rather reflects the temporal variation of the emissions from all sources. The diurnal course of hourly PM2.5 concentrations on days having 24 h-average PM2.5 concentrations less than 25
Over the nonattainment area, REF monthly average PM2.5 concentrations were 12.9, 11.0, 9.2, 11.0, 9.8, and 5.7
The on-average high PM2.5 emissions (188.3 g.km-2 h−1) and relative low wind speeds (1.9 m.s−1) over the nonattainment area in October led to the highest monthly average PM2.5 concentrations of October to March. On monthly average, wind speed and ABL-height were lowest (0.9 m.s−1 and 122.7 m at the grid cell holding the monitoring site, respectively) in November, which explains the high monthly average PM2.5 concentrations despite of the on-monthly-average second lowest PM2.5 emissions of October to March. In March, the on-average relatively high wind speed and ABL height (2.6 m.s−1 and 567.2 m at the grid-cell of the monitoring site) provided good dilution and transported polluted air out of the nonattainment area, which yielded low PM2.5 concentration over the nonattainment area.
In REF, all maximum 24 h-average PM2.5 concentrations obtained on any day during October to March occurred in the nonattainment area. Of the 182 days, the highest 24 h-average PM2.5 concentrations occurred at the grid-cell holding the monitoring site and/or the grid cells adjacent to it to the south and west (these three grid cells are called site group hereafter) on 86, 64, and 32 days, respectively. This fact is due to relative strong PM2.5 emissions in these grid cells in comparison with other grid cells in the nonattainment area. The site group PM2.5 emissions made up 34.3% of the total emissions in the nonattainment area that encompasses 31 grid cells.
In REF, 55 exceedance days and 131 exceedances were simulated during October to March, of which 52 exceedances occurred at the grid cell of the monitoring site. The number of exceedance days (exceedances) in October, November, January, February, and March was 20 (57), 10 (13), 5 (13), 15 (37), 5 (11), and 0 (0), respectively. All exceedances typically occurred in the site group. The highest and lowest 24 h-average PM2.5 concentrations on any exceedance day were 72.2 and 35.1
Exceedances typically occurred when at least any two of the following conditions coexisted: strong emission rate (>3600 g.km-2 h−1), low wind speed (
On days with calm wind (<0.5 m.s−1), high 24 h-average PM2.5 concentrations and often exceedances occurred in the nonattainment area and its surrounding area (Figure
Zoom-in on typical wind circulation patterns at breathing level associated with high and low PM2.5 concentrations in the nonattainment area in October to March. The contour lines represent the potential temperature gradient
On all except eight days, the highest 24 h-average PM2.5 concentrations occurred at the same grid cells in WSR and REF. On those eight days, the 24 h-average PM2.5 concentration maxima in WSR, however, still occurred within the site group like in REF. The slight shifts in position of the local maxima were due to marginal (in the order of measurement accuracy) changes in meteorological conditions due to indirect and direct feedback between the aerosol concentrations and radiation.
In WSR, the monthly average PM2.5 concentrations in the nonattainment area were 12.2, 10.3, 8.6, 10.3, 9.2, and 5.3
The highest 24 h-average PM2.5 difference obtained anywhere in the domain was 5.7
In the nonattainment area over October to March, about 45% and 33% of the 24 h-average PM2.5 differences fell between 0.5–1
Population distribution of 24 h-average PM2.5 difference in the nonattainment area as obtained for WSR in each month. The occurrences of all 24 h-average PM2.5 differences <0.0
On the nine days when the maximum 24 h-average PM2.5 concentrations exceeded 60
On 111 out of the 182 days, the maximum 24 h-average PM2.5 difference occurred within the site group. The maximum 24 h-average PM2.5 differences typically occurred in the site group on days with calm winds (
In the nonattainment area at grid-cells with strong PM2.5 emissions (>1400 g.km-2 h−1), the 24 h-average PM2.5 differences strongly depended on the PM2.5 emission reduction (
PM2.5 speciation in REF hardly differed from that in WSR (<0.1%). The low changes in the partitioning among SO4, NO3, and other PM2.5 species was partly due to the low emission reductions, the low availability of NH3 and low shortwave radiation in Fairbanks during October to March.
In WSR, 1 (8), 3 (5), 2 (3), 1 (8), 0 (0), and 0 (0) exceedance days (exceedances) were avoided in October, November, December, January, February, and March, respectively, as compared to REF. Out of them eight exceedances were avoided at the grid cell holding the monitoring site. On all exceedance-days except February 8, 2009, the locations of exceedances were identical in WSR and REF. On February 8, 2009, more grid-cells experienced exceedances in WSR than REF (three versus two grid-cells) due to the close to 35
At exceedance locations, about 18.3, 9.9, 42.0, 22.1, 10.7, and 6.1% of the 24 h-average PM2.5 differences varied between <2, 2-3, 3-4, 4-5, and >5
At the grid-cell of the monitoring site the RRFs of 24 h-average PM2.5 concentrations were 0.951, 0.950, 0.952, 0.956, 0.941, and 0.940 in October, November, December, January, February, and March, respectively. At this grid-cell, the daily RRFs of 24 h-average PM2.5 concentration were 0.938, 0.949, and 0.965 at the 50th, 75th, and 90th percentile, respectively. These findings suggest that the RRFs of total PM2.5 concentrations at the grid-cell of the monitoring site were relatively consistent throughout October to March. The overall RRFs for NO3 were 0.835, 0.893, 0.913, 0.868, 1.035, and 0.873 in October to March, and 0.866, 0.897 and 0.960 at the 50th, 75th, and 90th percentile, respectively. The RRF of NO3 greater than 1 may be an artifact related to the very low NO3 concentrations (<1
Similar RRFs as obtained for the grid-cell of the monitoring site were also obtained for the other grid-cells of the site group. At the other grid-cells in the nonattainment area, the RRFs of all PM2.5 species were slightly decreased (increased) as compared to that of the grid-cell with the monitoring site when those grid-cells were located in the upwind (downwind) of the site group. For all species, the RRFs obtained at these other grid-cells in the nonattainment area varied about ±0.1 of the RRFs obtained at the grid-cell of the monitoring site. The grid-cells with the lowest RRFs, that is, lowest reduction, were typically located along the boundary of the nonattainment area and in the upwind of grid-cells with high pollution. The grid-cells along the boundary of the nonattainment area namely experienced frequently clean air advection from outside the nonattainment area. Therefore, the emission reductions related to the changeout of wood-burning devices hardly affected them. The grid-cells with the highest RRFs typically occurred inside the nonattainment area and had low 24 h-average PM2.5 concentrations (<4
The benefits of the changeout of wood-burning devices on the 24 h-average PM2.5 concentrations drastically decreased outside and downwind of the nonattainment area. At radii of 4 km, 8 km, 12 km, and 16 km downwind of the nonattainment area, the 24 h-average PM2.5 differences were about 27.5, 13.1, 7.3, and 4.6% of the 24 h-average PM2.5 differences obtained on average over the nonattainment area. A
Zoom-in on the average differences of PM2.5 concentration between REF and WSR for October to March. Hashed shading indicates grid cells with significant differences at the 95% or higher level of confidence.
WSS1 represents a large emission reduction (Figure
The maximum 24 h-average PM2.5 concentrations obtained in REF, WSR, WSS1, and WSS2 on any day of the 14d sensitivity study were 51.1, 47.6, 26.9, and 47.5
Like Figure
The average RRFs of the 24 h-average PM2.5 concentrations obtained at the grid-cell of the monitoring site for WSS1, WSS2, and WSR were 0.543, 0.913, and 0.930, respectively, for the 14d episode. The RRFs of NH4 were about 1 in all sensitivity simulations. The RRFs of NO3 were 0.471, 0.815, and 0.818 in WSS1, WSS2 and WSR, respectively, while those of SO4, OC, EC, and others were similar to those for PM2.5.
The spatial variations of RRFs were within ±0.1 of the RRF at the grid-cell of the monitoring site for any species at any grid-cell in the nonattainment area for both WSS2 and WSR. On the contrary, in WSS1, the spatial variations of RRFs reached from no difference to 0.4 greater RRF values than the RRF-value at the grid-cell of the monitoring site. On six and five out of the 14 days of the sensitivity study, the highest response, that is, highest reduction in the nonattainment area, occurred at the grid-cell of the monitoring site and other grid-cells of the site group. The highest response (
The high number of wood-burning devices changed out in WSS1 led to avoidance of all 4 (6) exceedance days (exceedances) that occurred in REF during the same time. No exceedances were avoided in both WSS2 and WSR during these 14 days. The highest (lowest) 24 h-average PM2.5 difference obtained at any exceedance location in WSS1 was 24.9 (16.8)
The effects of exchanging noncertified wood-burning devices with certified woodstoves on reducing the 24 h-average PM2.5 concentrations at breathing level in the Fairbanks nonattainment area were investigated for October 1, 2008, to March 31, 2009, using results from WRF/Chem simulations. The results indicated that the assumed wood-burning device changeouts helped to reduce the 24 h-average PM2.5 concentrations at breathing level in the nonattainment area. However, the reduction effectiveness depends on the number of wood-burning devices changed out and what kinds of devices are changed out. The wood-burning device changeout scenario based on data reported by [
The magnitude of the 24 h-average PM2.5 differences REF-WSR differed strongly among days and locations. High 24 h-average PM2.5 differences (>3
The wood-burning device changeout assumed in WSR only effectively helped to avoid 7 out of 55 exceedance days that occurred in REF. Moreover, this avoidance occurred only on days with 24 h-average PM2.5 concentration slightly above 35
The 14d sensitive simulations assuming the number of wood-burning devices reported by [
The authors thank C. F. Cahill, G. Kramm, W. R. Simpson, G. A. Grell, K. Leelasakultum, T. T. Tran, and the anonymous reviewers for fruitful discussion. This research was in part supported by the Fairbanks North Star Borough under contract LGFEEQ. Computational resources were provided by the Arctic Region Supercomputing Center.