{"id":34220,"date":"2025-02-27T19:45:32","date_gmt":"2025-02-27T18:45:32","guid":{"rendered":"https:\/\/www.co2news.sk\/?p=34220"},"modified":"2025-02-27T19:45:55","modified_gmt":"2025-02-27T18:45:55","slug":"urban-trees-prove-to-absorb-significantly-more-carbon-than-previously-thought","status":"publish","type":"post","link":"https:\/\/www.co2news.sk\/en\/2025\/02\/27\/urban-trees-prove-to-absorb-significantly-more-carbon-than-previously-thought\/","title":{"rendered":"Urban trees can absorb significantly more carbon than previously thought."},"content":{"rendered":"<p><a href=\"https:\/\/pubs.acs.org\/doi\/epdf\/10.1021\/acs.est.4c11392?ref=article_openPDF\" target=\"_blank\" rel=\"noopener\"><span style=\"color: #0000ff;\">Document<\/span><\/a>, \u201cObserving Anthropogenic and Biogenic CO2 Emissions in Los Angeles Using a Dense Sensor Network,\u201d written by Jinsol Kim et al., deals with the quantification of urban CO2 emissions <!--more-->with a precision that allows seasonal and annual trends to be tracked. The study uses data from twelve BEACO2N (Berkeley Environmental Air-quality &amp; CO2 Observation Network) sites in Los Angeles, which are part of the USC Carbon Census, and analyzes them using a box model.<\/p>\n<p><strong>Key points of the study:<\/strong><\/p>\n<ul>\n<li><strong>Monitoring systems in cities:<\/strong> Urban areas contribute significantly to greenhouse gas emissions, so it is important to monitor and evaluate the effectiveness of emission reduction strategies.<\/li>\n<li><strong>Emission estimation methods:<\/strong> There are two main approaches to estimating CO2 emissions:\n<ul>\n<li>&quot;Bottom-up&quot;: estimate total emissions using economic indicators or by mapping emission sources and measuring activities.<\/li>\n<li>&quot;Top-down&quot;: estimates emissions based on measurements of atmospheric CO2 and atmospheric transport modeling.<\/li>\n<\/ul>\n<\/li>\n<li><strong>BEACO2N:<\/strong> This network is designed to create high spatial resolution maps of urban air while minimizing capital and operational costs. It consists of low-cost sensors for measuring CO2, CO, NO2, NO, O3 and aerosols.<\/li>\n<li><strong>Box Model:<\/strong> The study uses a box model to quantify total CO2 emissions in the Los Angeles area. Measurements from locations along the prevailing wind direction are combined with meteorological information.<\/li>\n<li><strong>Emissions breakdown:<\/strong> Total CO2 emissions are divided into fossil fuel emissions and biogenic emissions using carbon monoxide (CO) measurements as an indicator of fossil fuel CO2 emissions. The CO\/CO2 ratio is used to estimate fossil fuel CO2 emissions.<\/li>\n<li><strong>Biogenic influence:<\/strong> The study found that biogenic exchange can consume up to 60 % of fossil fuel emissions during daylight hours during the growing season.<\/li>\n<li><strong>Effective mixing height:<\/strong> Experiments with synthetic data help determine the effective mixing height, which affects the accuracy of emission estimates. Daily fluxes are found to show reasonable agreement at h = 0.3\u20130.4 hHRRR.<\/li>\n<li><strong>Seasonal variations:<\/strong> The observed seasonal changes in biogenic emissions correlate with the Enhanced Vegetation Index (EVI). Higher emissions from fossil fuels are observed from January to June, which may be related to the use of natural gas for heating in winter.<\/li>\n<li><strong>Uncertainties:<\/strong> The study assesses uncertainties caused by various assumptions in the model and uncertainties in sensor measurements, meteorological data, and the CO\/CO2 ratio.<\/li>\n<li><strong>Conclusion:<\/strong> The study demonstrates the feasibility of using a dense sensor network and a box model to monitor urban CO2 emissions and separate them into anthropogenic and biogenic components. The approach is simpler compared to computationally intensive inversion methods.<\/li>\n<\/ul>\n<p>The study also suggests that the BEACO2N network can be used in other cities to assess long-term trends in CO2 emissions and other pollutants. <em><strong>Spring<\/strong><\/em><\/p>\n<hr \/>\n<p><strong>Glossary of Key Terms<\/strong><\/p>\n<ul>\n<li><strong>Anthropogenic emissions:<\/strong> Emissions caused by human activities, such as the burning of fossil fuels.<\/li>\n<li><strong>Biogenic emissions:<\/strong> Emissions originating from biological sources such as vegetation and soil (including plant respiration).<\/li>\n<li><strong>BEACO2N:<\/strong> Berkeley Environmental Air-quality &amp; CO2 Observation Network; a dense network of sensors to monitor air quality and CO2.<\/li>\n<li><strong>Box Model:<\/strong> A simplified atmospheric model that assumes uniform mixing of emissions within a defined volume.<\/li>\n<li><strong>CO2ff:<\/strong> Fossil CO2; CO2 produced by burning fossil fuels.<\/li>\n<li><strong>EVI (Enhanced Vegetation Index):<\/strong> Highlighted Vegetation Index; a measure of the greenness of vegetation cover used as a proxy for biogenic carbon uptake.<\/li>\n<li><strong>Flux:<\/strong> Flux; the amount of substance that passes through a certain area per unit time (\u03bcmol m-2 s-1).<\/li>\n<li><strong>HRRR (High-Resolution Rapid Refresh):<\/strong> NOAA meteorological model providing estimates of meteorological parameters such as PBL height and wind speed.<\/li>\n<li><strong>Hestia-LA:<\/strong> High-resolution CO2 fossil fuel emissions product for the Los Angeles metropolitan area.<\/li>\n<li><strong>In Situ Calibration:<\/strong> Calibration directly in the field, at the measurement location.<\/li>\n<li><strong>Inverse Modeling:<\/strong> Inverse modeling; a technique for estimating emissions using atmospheric measurements and transport models.<\/li>\n<li><strong>LAMC (Los Angeles Megacity Carbon Project):<\/strong> A project to measure and analyze carbon flows in the Los Angeles metropolitan area.<\/li>\n<li><strong>PBLH (Planetary Boundary Layer Height):<\/strong> Planetary boundary layer height; mixing layer height.<\/li>\n<li><strong>Rff:<\/strong> Ratio of CO emissions to fossil CO2 emissions (CO\/CO2ff).<\/li>\n<li><strong>STILT (Stochastic Time-Inverted Lagrangian Transport):<\/strong> Atmospheric transport model used to calculate footprints that indicate receptor sensitivity to surface emissions.<\/li>\n<li><strong>Top-Down Approach:<\/strong> Top-down approach; estimating emissions using atmospheric measurements and transport models.<\/li>\n<li><strong>Bottom-Up Approach:<\/strong> Bottom-up approach; estimating emissions using economic indicators or detailed information on emission sources.<\/li>\n<\/ul>","protected":false},"excerpt":{"rendered":"<p>The paper, \u201cObserving Anthropogenic and Biogenic CO2 Emissions in Los Angeles Using a Dense Sensor Network,\u201d written by Jinsol Kim et al., deals with quantifying urban CO2 emissions.<\/p>","protected":false},"author":7,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-34220","post","type-post","status-publish","format-standard","hentry","category-znizovanie_co2_cdr_ccs_ccu_dac"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.co2news.sk\/en\/wp-json\/wp\/v2\/posts\/34220","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.co2news.sk\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.co2news.sk\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.co2news.sk\/en\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.co2news.sk\/en\/wp-json\/wp\/v2\/comments?post=34220"}],"version-history":[{"count":0,"href":"https:\/\/www.co2news.sk\/en\/wp-json\/wp\/v2\/posts\/34220\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.co2news.sk\/en\/wp-json\/wp\/v2\/media?parent=34220"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.co2news.sk\/en\/wp-json\/wp\/v2\/categories?post=34220"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.co2news.sk\/en\/wp-json\/wp\/v2\/tags?post=34220"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}