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Characterizing ozone sensitivity to urban greening in Los Angeles under current day and future anthropogenic emissions scenarios

Authors: Schlaerth, Hannah L.;

Characterizing ozone sensitivity to urban greening in Los Angeles under current day and future anthropogenic emissions scenarios

Abstract

This dataset contains outputs from WRF-Chem version 3.7 and a jupyter notebook (Python 3.0) script that were used to generate the figures included in the manuscript. Each numpy file contains data from July 1, 2012 00:00 LST - July 31, 2012 23:00 LST. Dataset contents and naming conventions are described below. The directory called "pubFigs" contains two jupyter notebooks that generate the figures referenced in the main text (Final_Figures.ipynb) and supplementary information (Final_SI_Figs.ipynb). Figures are generated using processed WRF output data for each of the simulations described in the manuscript. Files that end with "2012runbc.npy" correspond to the Baseline simulation Files ending with "GVF.npy" correspond to the GVF50 simulation Files ending with "BVOC_Low", "BVOC_Med", and "BVOC_High" correspond to the low, medium, and high BVOC emissions scenarios Files ending with "LA100" used the future anthropogenic emissions scenario Files that do not end with "LA100" used the current day anthropogenic emissions scenario An additional file called "Pouya_LU.npy" contains the land cover/ land use codes from our model configuration and is used to plot diurnal cycles of urban and nonurban land cover. Dataset contents and units: apinene_hourly_[simulation name].npy: hourly alpha-pinene emissions; mol/km2/hr bpinene_hourly_[simulation name].npy: hourly beta-pinene emissions; mol/km2/hr co_hourly_[simulation name].npy: hourly CO concentrations; ppm Daily8HourMaxO3_[simulation name].npy: daily maximum 8-hour O3 concentrations; ppb delta_dm8ho3_census_tract_avgs.npy: Change in daily maximum 8-hour O3 concentrations aggregated to the census tract level; ppb isoprene_hourly_[simulation name].npy: hourly isoprene emissions; mol/km2/hr msebio_iso_[simulation name].npy: base isoprene emission factors used as inputs to MEGAN; mol/km2/hr NO2_hourly_[simulation name].npy: hourly NO2 concentrations; ppm NO_hourly_[simulation name].npy: hourly NO concentrations; ppm O3_hourly_[simulation name].npy: hourly O3 concentrations; ppm pftp_bt_[simulation name].npy: gridded plant functional type distribution for broad leaf trees used as inputs to MEGAN; % pftp_nt_[simulation name].npy: gridded plant functional type distribution for needle leaf trees used as inputs to MEGAN; % gvf_[simulation name].npy: pixel-level green vegetation fraction; unitless T2_hourly_[simulation name].npy: 2m air temperature; K wspd_hourly_[simulation name].npy: windspeed; m/s wrfinput_d03: input file for WRF that is used for plotting urbfrac_2012runbc.npy: urban fraction data for all simulations; unitless pop_joined_wrfgrid.csv: WRF grid indices/latitudes and longitude matched to census tract IDs

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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