*Air pollutant emissions by specie in 2050 (Gg)*
diff --git a/vignettes/Module2_concentration.Rmd b/vignettes/Module2_concentration.Rmd index 8ec2d27..dae547f 100644 --- a/vignettes/Module2_concentration.Rmd +++ b/vignettes/Module2_concentration.Rmd @@ -150,7 +150,7 @@ library(magrittr) ``` -In addition, for all these function, the package allows to produce different figures and/or animations, generated using the [rmap](https://github.com/JGCRI/rmap) package documented in the following [page](jgcri.github.io/rmap/). To generate these maps, the user needs to include the `map=T` parameter, and they will be generated and stored in the corresponding output sub-directory. As an example for this module, the following map shows the PM2.5 concentrations in 2050: +In addition, for all these functions, the package allows to produce different figures and/or animations, generated using the [rmap](https://github.com/JGCRI/rmap) package documented in the following [page](jgcri.github.io/rmap/). To generate these maps, the user needs to include the `map=T` parameter, and they will be generated and stored in the corresponding output sub-directory. As an example for this module, the following map shows the PM2.5 concentrations in 2050:*PM2.5 concntration by region in 2050 (ug/m3)*
@@ -158,7 +158,7 @@ In addition, for all these function, the package allows to produce different fig -As indicated in the documentation of TM5-FASST [Van Dingenen et al 2018](https://acp.copernicus.org/articles/18/16173/2018/acp-18-16173-2018-discussion.html),estimates of PM2.5 and O3 concentration levels in a receptor region driven by the emissions of different precursors in different sources are based on parametrizations of meteorology and atmospheric chemistry drawn from the more complex TM5 model. In summary, as explained in [Sampedro et al 2020](https://www.sciencedirect.com/science/article/pii/S1352231020302739), concentrations of a pollutant $j$, in region $y$, from all the precursors ($i$) emitted in all regions ($x_k$), is calculated as: +As indicated in the documentation of TM5-FASST [Van Dingenen et al 2018](https://acp.copernicus.org/articles/18/16173/2018/acp-18-16173-2018-discussion.html),estimates of PM2.5 and O3 concentration levels in a receptor region driven by the emissions of different precursors in different sources are based on parametrizations of meteorology and atmospheric chemistry drawn from the more complex TM5 model. In summary, concentration of a pollutant $j$, in region $y$, from all the precursors ($i$) emitted in all regions ($x_k$), is calculated as: $$C_j (y)=C_{j,base} (y)+∑_{k=1}^{n_x}∑_{i=1}^{n_i}SRC_{i,j} [x_k,y]\cdot[E_i (x_k)-E_{i,base} (x_k )]$$ @@ -187,7 +187,7 @@ Following this equation, base-run emissions and concentrations, and source-recep + AOT40: NOx, NMVOC, SO2 and CH4 + Mi (M7 and M12): NOx, NMVOC, SO2 and CH4 -The packages also includes the base emission and concentration levels for all these indicators. +The package also includes the base emission and concentration levels for all these indicators. In addition, primary PM2.5 emissions (BC and POM) are assumed to have a more direct influence in urban (more dense) areas, so the emission-concentration relation for these two pollutants is modified using adjustment coefficients that are included in the package. diff --git a/vignettes/Module3_health.Rmd b/vignettes/Module3_health.Rmd index da25d59..b9082d7 100644 --- a/vignettes/Module3_health.Rmd +++ b/vignettes/Module3_health.Rmd @@ -13,7 +13,7 @@ knitr::opts_chunk$set( ) ``` -The functions in this module estimate adverse health effects attributable to fine particulate matter (PM2.5) and ozone (O3; M6M) exposure, measured as premature mortalities, years of life lost (YLLs), and disability adjusted life years (DALYs). The following code shows as an example, the functions to estimate premature mortalities attributable to PM2.5 and the DALYs associated with ozone exposure (M6M).All the functions associated to this module can be found in the [References] (https://jgcri.github.io/rfasst/reference/index.html) page. +The functions in this module estimate adverse health effects attributable to fine particulate matter (PM2.5) and ozone (O3; M6M) exposure, measured as premature mortalities, years of life lost (YLLs), and disability adjusted life years (DALYs). The following code shows as an example, the functions to estimate premature mortalities attributable to PM2.5 and the DALYs associated with ozone exposure (M6M).All the functions associated to this module can be found in the [References](https://jgcri.github.io/rfasst/reference/index.html) page. ```{r setup_mort} @@ -100,7 +100,8 @@ The complete list of outputs generated by the suite of functions that form this * `O3_MORT_ECOLOSS_[scenario]_[year].csv` * `O3_YLL_ECOLOSS__[scenario]_[year].csv` -As in Module 2, for all these function, the package allows to produce different figures and/or animations, generated using the [rmap](https://github.com/JGCRI/rmap) package documented in the following [page](jgcri.github.io/rmap/). To generate these maps, the user needs to include the `map=T` parameter, and they will be generated and stored in the corresponding output sub-directory. As an example for this module, the following map shows the premature mortalities attributable to PM2.5 exposure by cause in 2050 +As in Module 2, for all these functions, the package allows to produce different figures and/or animations, generated using the [rmap](https://github.com/JGCRI/rmap) package documented in the following [page](jgcri.github.io/rmap/). To generate these maps, the user needs to include the `map=T` parameter, and they will be generated and stored in the corresponding output sub-directory. As an example for this module, the following map shows the premature mortalities attributable to PM2.5 exposure by cause in 2050. +*Premature Mortalities attributable to PM2.5 concentration by cause in 2050 (#)*
diff --git a/vignettes/Module4_agriculture.Rmd b/vignettes/Module4_agriculture.Rmd index b7c7c29..ff762a4 100644 --- a/vignettes/Module4_agriculture.Rmd +++ b/vignettes/Module4_agriculture.Rmd @@ -25,13 +25,13 @@ $$RYL_{t,r,j}=1-\frac{exp~[-(\frac{M_{t,r,j}}{a_j})^{b_j}]}{exp~[-(\frac{c_j}{a_ More information on the methodology used for the estimation of agricultural damages can be found in the TM5-FASST documentation paper ([Van Dingenen et al (2018)](https://acp.copernicus.org/articles/18/16173/2018/acp-18-16173-2018-discussion.html)) and in [Van Dingenen et al (2009)](https://www.sciencedirect.com/science/article/pii/S1352231008009424?via%3Dihub). The combined use of the models for estimating agricultural damages is explained in more detail in [Sampedro et al (2020)](https://www.sciencedirect.com/science/article/pii/S1352231020302739). -Production losses are calculated combining the RYLs with projected agricultural production levels of the analyzed GCAM scenario. In addition, multiplying the projected price by the production losses (quantities), we estimate revenue losses for period t, region i, and crop j as: +Production losses are calculated combining the RYLs with projected agricultural production levels of the analyzed GCAM scenario. In addition, multiplying the projected price by the production losses (quantities), we estimate revenue losses for period $t$, region $i$, and crop $j$ as: $$Damage_{t,r,j}=Prod_{t,r,j} \cdot Price_{t,r,j} \cdot Pulse.RYL_{t,r,j}$$ As explained above, the module combines O3 concentration data with agricultural production and price projections. However, in order to complete all the calculations, the package includes some additional information: * 2010 data on harvested area for downscalling the damages to country level in order to re-scale them to the corresponding regional disaggregation (`d.ha`;`area_harvest.csv`) -* RYLs based on the different exposure-response functions are calculated for four main crops (maize, rice, soybeans, and wheat), so the damages need to be expanded to the rest of the commodities. The package includes a commodity mapping that expands the damages to all the crops based on their carbon fixation pathway (C3 or C4 categorization) (`d.gcam.commod.o3`; `GCAM_commod_map_o3_JS.csv`) +* RYLs based on the different exposure-response functions are calculated for four main crops (maize, rice, soybeans, and wheat), so the damages need to be expanded to the rest of the commodities. The package includes a commodity mapping that expands the damages to all the crops based on their carbon fixation pathway (C3 or C4 categorization) (`d.gcam.commod.o3`; `GCAM_commod_map_o3.csv`) We note that these two files could be easily modified by the user if any other mapping/downscalling technique is preferred. The files are stored in the `/inst/extada/mapping` folder. @@ -88,7 +88,8 @@ library(magrittr) ``` -As in Modules 2 and 3, for all these function, the package allows to produce different figures and/or animations, generated using the [rmap](https://github.com/JGCRI/rmap) package documented in the following [page](jgcri.github.io/rmap/). To generate these maps, the user needs to include the `map=T` parameter, and they will be generated and stored in the corresponding output sub-directory. As an example for this module, the following map shows the preature mortalities attributable to PM2.5 exposure by cause in 2050 +As in Modules 2 and 3, for all these functions, the package allows to produce different figures and/or animations, generated using the [rmap](https://github.com/JGCRI/rmap) package documented in the following [page](jgcri.github.io/rmap/). To generate these maps, the user needs to include the `map=T` parameter, and they will be generated and stored in the corresponding output sub-directory. As an example for this module, the following map shows the production losses attributable to O3 exposure in 2050. +*Agricultural production losses attributable to O3 exposure by commodity in 2050 (Mt)*