TOWARDS THE REGULATION OF NON-ROAD DIESEL EMISSIONS IN AUSTRALIA

expectancy, and therefore some of the overall health benefit would accumulate after 2043. The analysis of future health benefits therefore considered the lag between changes in exposure and changes in acute, medium-term and chronic health outcomes over a period of NRDE emission reductions to 2043, and also from 2043 to 2063.

3. Methodology

3.1. Overview

A national impact pathway modelling approach was used to evaluate the effects of the management scenarios. This involved detailed modelling of the following for NRDEs, as well as other major anthropogenic and natural sources:

 Emissions, with allocation in space and time.

 Atmospheric dispersion, chemistry and particle microphysics.

 Exposure to air pollution, which involved linking spatial pollution and population data.

 The impacts of air pollution on life expectancy.

3.2. Stock and activity modelling

The national stock and activity of NRDEs were defined in detail for the three datum years. The basis for this was the stock model from Kinrade et al (2020), which provided the number of engines by equipment type (27 types), power band (10 bands), emission standard (6 US tiers) and age (40 years). The stock model was combined with activity data for each type of equipment, including median life (hours), usage (hours/year) and load factor (%). Discussions with industry stakeholders were used to refine the assumptions and data.

The effects of the management scenarios on the NRDE stock in 2028 and 2043 were simulated using an ‘industry model’. The management scenarios only affected the distribution of the stock across tiers; they did not affect the total stock in each year, or the distribution across equipment type, power and year of manufacture.

3.3. Emission modelling

3.3.1. Model configuration and calibration

Emissions from NRDEs were estimated using the NONROAD2008a methodology (USEPA 2010). This involved a bottom-up calculation for the datum years, with predictions of annual emissions of CO,

THC, NOX, PM10, PM2.5, SO2 and CO2. This paper focusses on the results for NOX and PM2.5

The equipment types in the stock were also allocated categorically to economic sectors which were broadly equivalent to ANZSIC divisions in diesel use statistics. This permitted a calibration of the bottomup calculations according to actual diesel use at the national level. It also enabled NRDE diesel use to be allocated to jurisdictions and economic sectors within jurisdictions.

Figure 1 illustrates the energy/diesel use of NRDEs in the national context using statistics for 2017-2018. The gross annual energy consumption in Australia (chart ‘a’) was 8,214 petajoules (PJ) (DoEE 2019). Of this, 1,111 PJ (14% of the national total) were associated with diesel. The analysis excluded transport sources, which accounted for 58% of diesel use (chart ‘b’), although recreational boats (‘marine domestic’) were retained. The remaining diesel use was allocated to NRDEs, and was then reorganised by economic sector (chart ‘c’). Finally, the national NRDE diesel use by sector was allocated to the various jurisdictions (chart ‘d’).

The annual diesel use and emissions by economic sector from the calibrated emission model were then allocated categorically to emission inventory source groups, so that the calibrated model could be linked to detailed spatial data on diesel use.

3.3.2. Spatial and temporal allocation

Various methods were devised to spatially allocate diesel use proportionally to jurisdictions and source groups. In NSW, which had the most detailed information on NRDEs, the spatial distributions from the state emissions inventory were used. In other jurisdictions, the proportion of diesel use by location was determined using various other methods. The main sources of spatial data in these cases were the National Pollutant Inventory (NPI) (2017-2018 reporting period) and state inventories. In some cases, the lack of information meant that spatial surrogates had to be used. For example, diesel use in construction was distributed spatially in proportion to employment in the construction sector using census data from ABS. Figure 2 shows the resulting locations of diesel use for NRDEs across Australia, along with a breakdown of the total by jurisdiction and sector.

The actual diesel use at each location was determined by multiplying the proportional use at the location by the total use from the emission model. This approach was also applied to emissions.

Temporal scaling factors were developed to adjust the annual emissions for NRDEs by hour of the day, day of the week and month of the year.

The emission model results were also used to define NRDE ‘intermediate year factors’ for each scenario. These were used to account for non-linearity in the emissions time series in the BAU scenario, and to enable the effects of each management scenario on health to be estimated more appropriately over the timeframe of the analysis.

3.4. Chemical transport modelling

Chemical transport modelling was used to simulate the patterns in air pollutant concentrations and exposure due to NRDE emissions. NRDE impacts were analysed against a backdrop of emissions from other anthropogenic and natural sources.

The CSIRO Chemical Transport Model (C-CTM) (Cope et al. 2004, Guérette et al. 2020) was used to determine spatially-defined annual concentrations of NO2 and PM2.5 for a base case which contained major anthropogenic emission sources other than NRDEs.

Figure 3 shows the C-CTM model domains. The CCTM simulations were undertaken for 12 one-way nested regional model domains (top pane) which spanned three spatial resolutions – 27 km, 9 km and 3 km. The highest resolution grid focussed on the regions of highest population density. The bottom pane shows the boundary concentrations for the regional modelling, derived from monthly average (for 2015) output from the Australian Community Climate and Earth System Simulator chemical transport model (Luhar et al. 2017). Shown are annual average ozone concentrations (ppb; here for a 2005 simulation) - one of the important species used to drive photochemistry in the regional model.

The base case predictions for annual mean NO 2 and PM2.5 across Australia were bias-corrected using a satellite and land-use regression (SatLUR) model (Knibbs et al. 2014, 2018). The effects of the NRDE BAU and management scenarios were then determined as perturbations to the base case, and this enabled the contribution of NRDEs to atmospheric concentrations to be quantified. The flow diagram in Figure 4 shows the sequence of steps involved.

Parameters other than NRDE emissions (e.g. meteorology, emissions from natural and other anthropogenic sources) were fixed for the timeframe of the analysis. The complex nature of atmospheric photochemical reactions means that, in reality, any future changes in emissions from other sectors would also influence the changes associated with

NRDEs. However, the approach was designed to be specific to NRDEs, so that changes in concentrations were due to changes in NRDE emissions only. A possible alternative approach, using (assumed) emission projections for other sectors, would have diminished the specificity for NRDEs. It would have been more difficult to disentangle the effects of future changes in emissions for multiple sectors, and would have introduced new uncertainty given the need to develop projections based on multiple assumptions.

The NRDE concentrations for 2018 and 2043 were combined with age- and location-specific population data at the Mesh Block level, and populationweighted NRDE concentrations – aggregated to Statistical Area Level 2 (SA2) – were entered into the health model.

3.5. Health modelling

3.5.1. General approach

Health impacts were assessed in relation to premature mortality, as this represents the majority of the monetised health costs of air pollution (e.g. USEPA 2011). Premature mortality was expressed as years of life lost (YLL) due to air pollutionattributable deaths in all age groups. State-level, age-specific (five-year age groups), all-cause mortality data were obtained from ABS (2017a, 2017b).

The concentration–response functions (CRFs) for mortality were taken from the HRAPIE project (Hoek et al. 2013) and COMEAP (Atkinson et al. 2018) (Table 2). These CRFs were consistent with those used in a recent study in NSW (Broome et al. 2020). For NO2, a health effect threshold of 5 µg/m3 was applied, with a log-linear concentration-response relationship down to this value, based on advice from Atkinson et al. (2018).

PM2.5 and NO2 are both produced by diesel engines (and other combustion sources), and their health impacts can therefore be hard to separate. To account for this non-independence we used the COMEAP (2015) approach, whereby a 20% reduction factor was applied to the NO 2 component.

3.5.2. Health burden of NRDEs in 2018 base year

The health burden in the 2018 base year (and in fact other recent years before 2018) was calculated assuming that 2018 population exposures were representative of those during the preceding decades under a static emissions situation.

To assess the YLL attributable to NRDE PM2.5 and NO2, the life expectancy for a hypothetical 2018 population without NRDEs was calculated by