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17 results for “Industrial change”
Model results and configuration files for "Large modeling uncertainty in projecting decadal surface ozone changes over urban and industrial regions of China"
<p>This repository includes files as described below:</p> <p><strong>1. namelist_CBMZ09_example.input, namelist_MOZART202_example.input:</strong></p> <p>Two WRF-chem namelist files for CBMZ and MOZART simulation.</p> <p>They are modified according to the namelist from <a href="https://github.com/wrfchem-leeds/WRFotron">https://github.com/wrfchem-leeds/WRFotron</a>.</p> <p><strong>2. wps_namelist_example.wps:</strong></p> <p>namelist for WRF Preprocessing System (WPS)</p> <p><strong>3. temporal_hourly_scale_factor_emission.csv:</strong></p> <p>Hourly scale factors for emissions.</p> <p>Hourly allocation is applied to all emission data (i.e., emissions for 2017, 2030 and perturbated emissions of NOx, VOCs).</p> <p><strong>4. vertical_emission_ratio.csv</strong></p> <p>Vertical shares (ratios) of emissions.</p> <p>Emissions from sectors of power and industry are vertically allocated based on this file. Vertical allocation is conducted for all emission data.</p> <p>These shares are suggested by MICS-ASIA III intercomparison framework.</p> <p><strong>5. 01_2030_2017_simulations.zip: </strong></p> <p>Simulated MDA8 ozone under future (2030) and 2017 emission scenarios by the two chemical mechanisms (i.e., CBMZ, MOZART).</p> <p><strong>6. 02_perturbations_of_NOxVOCs.zip:</strong></p> <p>Simulated MDA8 ozone given perturbations of NOx and VOCs emissions by the two chemical mechanisms.</p> <p><strong>7. 03_hourly_diff_O3_NOx_OH_HNO3.zip: </strong></p> <p>Differences of hourly simulated concentrations of O3, NOx, OH and HNO3 during July in the Base-2017 scenario between CBMZ and MOZART (CBMZ - MOZART).</p>
Chironomid taxa relative abundance information and lake identifiers for: Changes in midge assemblages reflect climate and trophic gradients across north temperate and boreal lakes since the pre-industrial period
<p>File 1: Relative abundances for chironomid taxa used in the manuscript: Changes in midge assemblages reflect climate and trophic gradients across north temperate and boreal lakes since the pre-industrial period. Lake_ID corresponds to the lake IDs attributed to each lake sampled as part of the LakePulse Network</p> <p>File 2: Lake_ID, lake name, latitude, longitude, sampling date, province, and ecozone for the 69 lakes examined in the manuscript: Changes in midge assemblages reflect climate and trophic gradients across north temperate and boreal lakes since the pre-industrial period. </p>
Dataset of pre-industrial climate from publication "Modeled storm surge changes in a warmer world: the Last Interglacial" by P. Scussolini et al.
<p>Results from the simulation of pre-industrial climate with climate model CESM1.2. Variables are: sea-level pressure (PSL); meridional wind (V), and zonal wind (U). Time step is 6-hourly.</p> <p>Detailed description of the methods are in the original publication:</p> <p>Scussolini, P., Dullaart, J., Muis, S., Rovere, A., Bakker, P., Coumou, D., Renssen, H., Ward, P. J., and Aerts, J. C. J. H.: Modelled storm surge changes in a warmer world: the Last Interglacial, EGUsphere, 2022, 1-20, 10.5194/egusphere-2022-101, 2022.</p>
Data and code for Global change drives modern plankton communities away from pre-industrial state
<p>Data and R code for "Global change drives modern plankton communities away from pre-industrial state" by Lukas Jonkers, Helmut Hillebrandt and Michal Kucera (https://doi.org/10.1038/s41586-019-1230-3).</p> <p>Compare planktonic foraminifera species assemblages from sediments and sediment traps.</p> <p>Scripts written by Lukas Jonkers</p> <p>DATA SOURCES<br> * HadISST: Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. Journal of Geophysical Research: Atmospheres 108, doi:10.1029/2002JD002670 (2003).<br> * ERSST v5: Huang, B. et al. NOAA Extended Reconstructed Sea Surface Temperature (ERSST), Version 5. Monthly mean. NOAA National Centers for Environmental Information. doi:10.7289/V5T72FNM. Access date: 14 Sep 2018. (2017).<br> * sediment assemblages: Siccha, M. & Kucera, M. ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples. Scientific Data 4, 170109, doi:10.1038/sdata.2017.109 (2017).<br> * sediment traps: citations provided in data files</p> <p>DATA<br> 1. Planktonic foraminifera shell flux time series<br> 1.1. all data: dat_sel.RDS<br> 1.2. time series with >125 and >150 micron data: dat_small.RDS<br> 1.3. shell flux data in csv format: shell_flux_data.csv</p> <p>2. ForCenS core top sediment assemblages<br> 2.1. all data (excluding duplicates and samples with incomplete taxonomy): forcens_trimmed_compare.RDS<br> 2.2. all data, split by region: species_domains_compare.RDS<br> 2.3. indices of samples: domain_indeces_compare.RDS</p> <p>3. SST<br> 3.1. average SST for each sample in ForCenS for 1870-1899 period based on HadISST: forcens_HadSST_1870-1899.RDS<br> 3.2. average SST for each sample in ForCenS for 1854-1883 period based on ERSST v5: forcens_ERSST_1854-1883.RDS<br> 3.3. average SST for each sediment trap site for 1870-1899 period based on HadISST: traps_HadSST_1870-1899.RDS<br> 3.4. average SST for each sediment trap site for 1854-1883 period based on ERSST v5: traps_ERSST_1854-1883.RDS<br> 3.5. average SST for each sediment trap site for deployment period based on HadISST: traps_HadSST_period.RDS<br> 3.6. average SST for each sediment trap site for deployment period based on ERSST v5: traps_ERSST_period.RDS<br> 3.7. linear SST trend between 1870 and 2015 based on HadISST: hadisst_trend_1870-2015.RDS<br> 3.8. average SST for period of sediment trap observations: hadisst_mean_1978-2013.RDS</p> <p>CODE<br> 1. get_ForCenS.R: selection of ForCenS data. Used to generate data 2.1-2.3<br> 2. make_polygons.R: make circles with 100 km radius around trap and core tope sites used in 4 and 5<br> 3. make_polygon_function.R: used by 2<br> 4. extract_HadSST.R: extraction of data 3.1, 3.3, 3.5, 3.7, 3.8<br> 5. extract_ERRSTv5.R: extraction of data 3.2, 3.4, 3.6<br> 6. make_annual_assemblages.R: process shell flux time series (data 1.1 and 1.2)<br> 7. make_annual_fluxes_function.R: used by 6<br> 8. compare_trap_sed_publish.R: code to compare sediment trap and core top assemblages<br> 9. compare_figs.R: code to create figures<br> 10. maps_robinson.R: code to create maps</p>
Data and Python script for article "A text mining analysis of the climate change literature in industrial ecology'
<p>The data and Python script are part of the forum article "A text mining analysis of the climate change literature in industrial ecology" authored by Dayeen, F.R., Sharma, A.S., and Derrible, S., and published in the <em>Journal of Industrial Ecology</em> in 2020.</p> <p>The Python script and instructions are included in the LiTCoF_v1.00-py.zip file. The original data is available in two formats: .csv and .pkl.</p> <p>Updates of the script will be posted at https://github.com/csunlab/LiTCoF and at https://csun.uic.edu/codes/LiTCoF.html. The data is also available at https://csun.uic.edu/datasets.html#AbstractsIE.</p> <p>Feel free to contact any of the authors for information and questions about the data and code.</p>
Industrial legacies in a rapidly changing Arctic
<p><strong>The file <em>Industrial_Legacies_Data.zip</em> contains the database and source codes used to investigate the impact of permafrost thaw on industrial legacies in the Arctic:</strong></p> <ul> <li>The "Data" folder contains several subfolders that are named by the figures in the paper "Thawing permafrost poses environmental threat to thousands of sites with legacy industrial contamination". In each of these folders all data files are provided to reproduce the underlying analysis and the figure itself.</li> <li>Furthermore the "Geospatial_DataCollection" folder contains the complete database used to analyze and visualize the occurences of industrial contamination in the Arctic.</li> <li>The "Scripts" folder contains several scripts that are again named by the figures in the paper. These scripts can be used to reproduce the analysis and to create the figures in the folder "Figures" and "Supplementary_Figures". Furthermore there are two scripts ("PermanentTalikYearAnalysis.py" and "TalikYearToShapefile.py") that were used to analyze the spatio-temporal talik development in the model domain.</li> </ul> <p><strong>The file <em>PanArctic_Simulations.zip</em> includes the source code of the CryoGrid permafrost model (Julia Language v. 0.6.4) and a start script with all parameters and forcing data (JSON) required to run the model for industrial sites located in the Arctic permafrost region.</strong></p> <p>Software:</p> <p>The Julia Language v.0.6.4 was used to run the simulations with the one-dimensional, transient permafrost model CryoGridLite. Furthermore, we used Python 3.7.4 for most of the data analysis and visualization.<br> The point process modelling used to relate industrial and contaminated sites and to create intensity maps of contaminated sites in the Arctic permafrost region is based on R version 3.6.3.</p>
Surface hourly measurement data of O3, NO2 and PM2.5 for "Large modeling uncertainty in projecting decadal surface ozone changes over urban and industrial regions of China"
<p>Surface hourly measurement data of O3, NO2 and PM2.5 during summer of 2017.</p> <p>In the .csv files, the first column contains the ID for each measurement site. "lon", "lat" are longitude and latitude, respectively.</p> <p>Date format is "YYYYMMDD_hour".</p>
Non-target screening of a Siberian ice core reveals changes in the pre-industrial to industrial organic aerosol composition
Open the record for dataset details and reuse information.
Historical data on industrial change and skills in Lithuania
<p>The data was created during a research project where the authors tried to assess the impact of radical industrial change on skills of workforce in Lithuania and other Central and Eastern European (CEE) countries between 1988 and 2008. The data at question, was predominantly used to evaluate the case of Lithuania. Currently the results of the research are in a process of being published. The research was funded by the Research Council of Lithuania (grant No. S-MOD-17-20).</p>
Change in gender roles as a factor in gender participation and empowerment in the oil mining industry: A case of Lokichar, Turkana county, Kenya
<p><span>This paper analyses a study done by the authors of this paper on changes on gender role as a factor in <em>gender participation and empowerment in the oil mining industry; a case of Lokichar in Turkana County</em>. The paper centres on three null hypothesis tested.</span> <span>The studies target group was the active </span>labour<span> force aged between 15 to 64 years. Those who retired from work were also targeted. The data was collected from a sample of three hundred (300) respondents selected through systematic random and purposive sampling methods. Focus Group Discussions (FGD) and in-depth interviews were conducted to supplement the questionnaires given to the sampled respondents. Chi-square was used to test the hypotheses. Major findings indicate that there is a relationship between</span> equal hiring and equal opportunity for men <span>and women to work in mining activities; </span>there is relationship between involvement in oil mining activities and change in livelihood and there is no relationship between involvement in oil mining activities and equal opportunity for men and women to work in mining activities<span>. Key recommendation include gender mainstreaming in legal frameworks, policies, Bills and programs.</span></p>
Supplementary material 1 from: Eschen R, Grégoire, JC, Hengeveld GM, de Hoop MB, Rigaux L, Potting RPJ (2015) Trade patterns of the tree nursery industry in Europe and changes following findings of citrus longhorn beetle, Anoplophora chinensis Forster. NeoBiota 26: 1-20. https://doi.org/10.3897/neobiota.26.8947
Table S1: Explanation note: Summary of dynamics of the import of Acer plants into the Netherlands in 1998–2012. The number of importing companies relates to the confirmed importers of Acer spp. HU indicates Hungary, AS indicates East-Asia and NZ indicates New Zealand.
Supplementary material 2 from: Eschen R, Grégoire, JC, Hengeveld GM, de Hoop MB, Rigaux L, Potting RPJ (2015) Trade patterns of the tree nursery industry in Europe and changes following findings of citrus longhorn beetle, Anoplophora chinensis Forster. NeoBiota 26: 1-20. https://doi.org/10.3897/neobiota.26.8947
Table S2: Explanation note: Summary of the intra-European trade in Acer plants from 138 producers in the Boskoop demarcated area in 2009, broken down by country.
Change in gender roles as a factor in gender participation and empowerment in the oil mining industry: A case of Lokichar, Turkana county, Kenya
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Data from: The first high-resolution aerosol pH change since industrial revolution constructed by the nitrogen isotopes of ice core ammonium
<p><span><span>Aerosol acidity has broad significance in the atmosphere and ecosystems; however, a reliable way to quantify its changes over long time is lacking. Here, we propose a n</span><span>ew</span><span> approach calculating aerosol pH based on the nitrogen stable isotope composition (δ15N) of aerosol ammonium (NH4+) and for the first time reconstruct historical trajectory of aerosol pH over the last two centuries via δ15N of NH4+ achieved in a Tibetan ice core. We observed a significant decrease in δ15N of ice core NH4+ by 13‰ from the preindustrial to modern era, corresponding to a decreased in aerosol pH by 0.75 units. The decline in pH demonstrates a dominant role of anthropogenic emissions in acid gases over alkaline gases since Industrial Revolution. Our study also suggests that spatiotemporal patterns of aerosol acidity could be widely revealed by future nitrogen stable isotope of aerosol</span><span> or ice core</span><span> ammonium measurement, which in turn will promote the understanding in aerosol chemistry in the context of global environmental changes.</span>y in the context of global environmental changes.</span></p>
Supplementary material 1 from: Rubenstein JM, Hulme PE, Rolston MP, Stewart AV, Hampton JG (2023) A century of weed change in New Zealand's forage seed multiplication industry. NeoBiota 85: 167-195. https://doi.org/10.3897/neobiota.85.100825
Supporting information
Data from: The first high-resolution aerosol pH change since industrial revolution constructed by the nitrogen isotopes of ice core ammonium
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Multi-omics analysis of mutagenized S. rimosus reveals synergistic changes that drive oxytetracycline production towards industrial efficiency and opens a new route to heterologous polyketides
GEO Series GSE232318. Streptomyces rimosus subsp. rimosus. 9 samples. Type: Expression profiling by high throughput sequencing.
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