Background conditions for an urban greenhouse gas network in the Washington, D.C. and Baltimore metropolitan region

A Gradient-Descent Optimization of CO2-CO-NOx Emissions over the Paris Megacity─The Case of the First SARS-CoV-2 Lockdown

Urban greenhouse gas emissions monitoring is essential to assessing the impact of climate mitigation actions. Using atmospheric continuous measurements of air quality and carbon dioxide (CO2), we developed a gradient-descent optimization system to estimate emissions of the city of Paris. We evaluated our joint CO2-CO-NOx optimization over the first SARS-CoV-2 related lockdown period, resulting in a decrease in emissions by 40% for NOx and 30% for CO2, in agreement with preliminary estimates using bottom-up activity data yet lower than the decrease estimates from Bayesian atmospheric inversions (50%). Before evaluating the model, we first provide an in-depth analysis of three emission data sets. A general agreement in the totals is observed over the region surrounding Paris (known as Île-de-France) since all the data sets are constrained by the reported national and regional totals. However, the data sets show disagreements in their sector distributions as well as in the interspecies ratios. The seasonality also shows disagreements among emission products related to nonindustrial stationary combustion (residential and tertiary combustion). The results presented in this paper show that a multispecies approach has the potential to provide sectoral information to monitor CO2 emissions over urban areas enabled by the deployment of collocated atmospheric greenhouse gases and air quality monitoring stations.

Methane Emissions Show Recent Decline but Strong Seasonality in Two US Northeastern Cities

Urban methane emissions estimated using atmospheric observations have been found to exceed estimates derived by using traditional inventory methods in several northeastern US cities. In this work, we leveraged a nearly five-year record of observations from a dense tower network coupled with a newly developed high-resolution emissions map to quantify methane emission rates in Washington, DC, and Baltimore, Maryland. Annual emissions averaged over 2018-2021 were 80.1 [95% CI: 61.2, 98.9] Gg in the Washington, DC urban area and 47.4 [95% CI: 35.9, 58.5] Gg in the Baltimore urban area, with a decreasing trend of approximately 4-5% per year in both cities. We also find wintertime emissions 44% higher than summertime emissions, correlating with natural gas consumption. We further attribute a large fraction of total methane emissions to the natural gas sector using a least-squares regression on our spatially resolved estimates, supporting previous findings that natural gas systems emit the plurality of methane in both cities. This study contributes to the relatively sparse existing knowledge base of urban methane emissions sources and variability, adding to our understanding of how these emissions change in time and providing evidence to support efforts to mitigate natural gas emissions.

Low-cost urban carbon monitoring network and implications for china: a comprehensive review

The development and renewal of gas sensor technology have enabled more and more low-cost gas sensors to form a carbon monitoring network to meet the requirements of the city. In the context of China's commitment to achieving the "double carbon" target by 2060, this paper reviews the principles of four standard gas sensors and the application of several low-cost sensors in urban carbon monitoring networks, with the aim of providing a practical reference for the future deployment of carbon monitoring networks in Chinese cities. Moreover, the types, prices, and deployment of the sensors used in each project are summarized. Based on this review, non-dispersive infrared sensors have the best performance among the sensors and are commonly used in many cities. Lots of urban climate networks in cities were summarized by many reviews in the literature, but only a few sensors were studied, and they did not consider carbon dioxide (CO2) sensors. This review focuses on the dense CO2 urban monitoring network, and some case studies are also discussed, such as Seoul and San Francisco. To address the issue of how to better ensure the balance between cost and accuracy in the deployment of sensor networks, this paper proposes a method of simultaneously deploying medium-precision and high-precision fixed sensors and mobile sensors to form an urban carbon monitoring network. Finally, the prospects and recommendations, such as different ways to mitigate CO2 and develop an entire carbon monitoring system for future urban carbon monitoring in China, are also presented.

The Impact of COVID-19 on CO2 Emissions in the Los Angeles and Washington DC/Baltimore Metropolitan Areas

Responses to COVID-19 have resulted in unintended reductions of city-scale carbon dioxide (CO2) emissions. Here, we detect and estimate decreases in CO2 emissions in Los Angeles and Washington DC/Baltimore during March and April 2020. We present three lines of evidence using methods that have increasing model dependency, including an inverse model to estimate relative emissions changes in 2020 compared to 2018 and 2019. The March decrease (25%) in Washington DC/Baltimore is largely supported by a drop in natural gas consumption associated with a warm spring whereas the decrease in April (33%) correlates with changes in gasoline fuel sales. In contrast, only a fraction of the March (17%) and April (34%) reduction in Los Angeles is explained by traffic declines. Methods and measurements used herein highlight the advantages of atmospheric CO2 observations for providing timely insights into rapidly changing emissions patterns that can empower cities to course-correct CO2 reduction activities efficiently.