# Scaling Urban Methane Emissions: Utility of Single-Site Measurements in Five Urban Domains

## TL;DR

This paper shows how methane emissions in cities can be estimated accurately using a few fixed measurement sites and a statistical method, improving understanding of urban methane sources.

## Contribution

A scalable Bayesian framework using single-site methane measurements to estimate urban emissions and identify infrastructure-based predictors.

## Key findings

- Single tower sites can provide robust methane emission estimates for cities.
- Residential building volume is a better predictor of emissions than population in some regions.
- The method addresses gaps in urban methane monitoring with minimal spatial coverage.

## Abstract

Urban methane (CH4) missions remain poorly
understood
due to limited observational constraints. Most estimates rely on bottom-up
inventories based on assumed emission factors and activity data or
downscaling methods, which often underestimate emissions, sometimes
by a factor of 2 or more in United States and European cities. While
satellite and mobile observations can improve understanding, they
face limitations in spatial resolution, coverage, and frequency. In
contrast, fixed in situ measurements calibrated to World Meteorological
Organization standards offer high precision continuous data, although
with limited spatial coverage due to logistical constraints. This
study uses in situ observations from single tower sites in five northeastern
United States cities to estimate total urban CH4 emissions
using a Bayesian scaling factor framework. Despite limited spatial
sampling, the approach yields robust emission estimates consistent
with other studies. To explore drivers of variability, the analysis
examines correlations between inferred emissions and urban characteristics
including population, residential gas usage, and infrastructure. Results
show that residential building volume outperforms population as a
predictor in some regions, highlighting the importance of infrastructure-specific
factors. By demonstrating a scalable observation-based approach using
minimal sites, this work addresses key gaps in urban CH4 monitoring and emphasizes the value of robust measurements and tailored
proxies for improving emission estimates in diverse urban settings.