Residential Emissions Research Articles

Effect of operating conditions and technology on residential wood stove emissions of criteria, greenhouse gas, and hazardous air pollutants

ABSTRACT Residential wood heating (RWH) is a known source of particulate matter (PM), hazardous air pollutants (HAPs), and greenhouse gases (GHGs). However, the influence of operating conditions on emissions from certified cordwood stoves in the United States (U.S.) remains poorly understood. This study analyzes emissions data from different operational phases, including start-up, high heat, and low heat, to improve indicators of real-world stove performance. We tested five commercially available U.S. stoves through the four distinct operational conditions or phases of the novel Integrated Duty Cycle (IDC) testing protocol, which simulates typical residential wood-burning patterns by incorporating start-up, high heat, medium (or “maintain) heat, and low heat (“overnight” burn) phases. We determined emissions factors (EFs) by IDC phase for criteria, GHG, and HAP compounds, including volatile organic compounds (VOCs) and polycyclic aromatic hydrocarbons (PAHs). We also developed a multiple linear regression model to assess the effect of dry burn rate (DBR) and IDC phase on each pollutant EF by stove technology type. IDC phase significantly influenced (p < 0.01) pollutant EFs from uncertified stoves and most emissions from catalytic/hybrid stoves, while DBR played a more substantial role in emissions from non-catalytic stoves. Current stove certification methods rely on a single nominal load under steady-state combustion, which does not reflect typical residential use. Additionally, we found DBR to be an inconsistent predictor of emissions in cordwood stoves. These findings underscore the importance of stove technology and operating conditions in determining RWH emissions, with implications for air quality science and regulatory policy. Implications Statement: Pollution from cordwood stoves includes fine particulate matter (PM), hazardous air pollutants (HAPs), greenhouse gases (GHGs), and other compounds that impact human health and climate. To better understand emissions under typical homeowner use patterns, we tested five U.S. cordwood stoves (four meeting 2020 New Source Performance Standards (NSPS) and one pre-NSPS, circa 1980 stove) across three technology types using the novel Integrated Duty Cycle (IDC) protocol. We report emissions factors by IDC phase, which better represent “real world” operating conditions (such as “start-up,” “high heat,” and “overnight burn”) and fuel loading patterns. We evaluated the effect of operating conditions and dry burn rate on PM, HAP and GHG emissions and determined significant effects from IDC phase in uncertified and catalytic/hybrid stove emissions, but not in noncatalytic stoves. This has important implications for use of emissions factors in air quality science, policy, and stove design, as different U.S. climate zones will influence the number of cordwood stove starts, fuel loading patterns, and the frequencies of the IDC phases.

The impact of town planning on direct residential CO2 emissions in rural China

The impact of town planning on direct residential CO2 emissions in rural China

Interval prediction model for residential daily carbon dioxide emissions based on extended long short-term memory integrating quantile regression and sparse attention

Interval prediction model for residential daily carbon dioxide emissions based on extended long short-term memory integrating quantile regression and sparse attention

Towards urban-rural development and equity in Western China: Insights gained from residential buildings' carbon emissions and per capita income

Towards urban-rural development and equity in Western China: Insights gained from residential buildings' carbon emissions and per capita income

Impact of urban morphology on dwellings carbon emissions at the district scale

Seeking low-carbon development in residential buildings is very important. We aimed to explore the mechanism of the urban form on residential building carbon emissions at the urban block level. When Beijing was adopted as a case study, blocks were categorized into enclosed, radial, intensive, and axial types. Geographic information data of 30 typical residential districts were extracted via ArcGIS software.Through the integration of parametric modeling and performance analysis techniques, the embodied and operational carbon emissions of typical districts were calculated. We analyzed carbon emission differences from the perspective of spatial factors such as the plot ratio, building density, and average building height, as well as temporally. The results revealed that the carbon emissions of the enclosed blocks were the lowest, and the carbon emissions of the intensive blocks were the highest. The plot ratio and average building height were negatively correlated with operation and embodied carbon emissions, whereas building density was positively correlated with operation and embodied carbon emissions.

Exploring the influencing mechanisms of residents’ income on residential building carbon emissions: Evidence from China

Exploring the influencing mechanisms of residents’ income on residential building carbon emissions: Evidence from China

Characterization of brown carbon absorption in different European environments through source contribution analysis

Abstract. Brown carbon (BrC) is a fraction of organic aerosol (OA) that absorbs radiation in the ultraviolet and short visible wavelengths. Its contribution to radiative forcing is uncertain due to limited knowledge of its imaginary refractive index (k). This study investigates the variability of k for OA from wildfires, residential, shipping, and traffic emission sources over Europe. The Multiscale Online Nonhydrostatic Atmosphere Chemistry (MONARCH) model simulated OA concentrations and source contributions, feeding an offline optical tool to constrain k values at 370 nm. The model was evaluated against OA mass concentrations from aerosol chemical speciation monitors (ACSMs) and filter sample measurements, as well as aerosol light absorption measurements at 370 nm derived from an Aethalometer™ from 12 sites across Europe. Results show that MONARCH captures the OA temporal variability across environments (regional, suburban, and urban background). Residential emissions are a major OA source in colder months, while secondary organic aerosol (SOA) dominates in warmer periods. Traffic is a minor primary OA contributor. Biomass and coal combustion significantly influence OA absorption, with shipping emissions also notable near harbors. Optimizing k values at 370 nm revealed significant variability in OA light absorption, influenced by emission sources and environmental conditions. Derived k values for biomass burning (0.03 to 0.13), residential (0.008 to 0.13), shipping (0.005 to 0.08), and traffic (0.005 to 0.07) sources improved model representation of OA absorption compared to a constant k. Introducing such emission source-specific constraints is an innovative approach to enhance OA absorption in atmospheric models.

Regional Disparities and Driving Factors of Residential Carbon Emissions: An Empirical Analysis Based on Samples from 270 Cities in China

Residential carbon emissions (RCEs) have become a major contributor to China’s overall carbon emission growth. A comprehensive analysis of the evolution characteristics of regional disparities in RCEs at the urban level, along with a thorough examination of the driving factors behind RCEs and the convergence, is crucial for achieving carbon reduction goals within regions. This study calculates the RCEs of 270 cities in China from 2011 to 2019 based on multiregional input–output tables and explores the regional differences and spatiotemporal evolution characteristics of RCEs using the Dagum Gini coefficient decomposition method and kernel density estimation. On this basis, we examine the driving factors of RCEs using an extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) econometric model and further analyze the convergence of RCEs by introducing a β-convergence model. The results are as follows: (1) The regional disparity of RCEs in China generally shows a wave-like declining trend, with the primary source of this disparity being the differences between city tiers. (2) Kernel density estimation shows that the greater the urban rank, the larger the regional disparity; the RCE distribution in third- and lower-tier cities is more concentrated. (3) Population density, population aging, and education level significantly exert a negative influence on RCEs, whereas economic development level, number of researchers, and number of private cars are positively correlated with RCEs. (4) Each urban agglomeration’s RCEs exhibits significant β-convergence, but the driving factors of RCEs and their convergence differ significantly across the urban agglomerations. This study provides targeted policy recommendations for China to achieve its emission reduction goals effectively. Each city cluster should tailor its approach to strengthen regional collaborative governance, optimize urban layouts, and promote low-carbon lifestyles in order to facilitate the convergence of RCEs and low-carbon transformation.

Long-Term Impacts of Global Solid Biofuel Emissions on Ambient Air Quality and Human Health for 2000-2019.

Globally, solid biofuels (SB) have been widely used for household cooking and energy production for decades due to electricity shortages and socio-economic barriers to adopting renewable energy alternatives. This has detrimental effects on air quality, human health, and climate through trace gas and aerosol emissions. Despite numerous studies, the long-term consequences of SB emissions remain poorly understood. Here, we use the Community Earth System Model and the Community Emissions Data System emission inventory to investigate the SB emission impacts on air quality and human health for 2000-2019. Global SB emission increased the ambient PM2.5 (particulate matter with aerodynamic diameters 2.5μm) and ozone (O3) concentrations up to 23.61 /m3 and 13.69ppbv, with significant effects found in India, China, and the Rest of Asia (ROA). Our study estimates total annual premature deaths (APDs) associated with global SB-attributable PM2.5 and O3 exposure as 1.11 million [95% confidence interval (95% CI): 1.00-1.22 million] in 2000 up to 1.43 million (95% CI: 1.30-1.56 million) in 2019. China's SB emissions and associated APDs have reduced substantially, whereas India and ROA had a major leap in both estimates in 2019 compared to 2000. China's progress in cutting residential SB emissions accounts for its improvements. Our study urges the reduction of SB usage and emissions to potentially improve overall air quality and human health conditions, especially in highly populated, low- and middle-income countries, where the poor air quality and associated health burden attributable to SB emissions are estimated to be higher.

Energy Consumption Patterns and Residential Carbon Footprint in Turkey

Purpose: The purpose of this article was to analyze energy consumption patterns and residential carbon footprint in Turkey. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: In Turkey, rapid urban growth and aging infrastructure have led to high residential carbon emissions primarily driven by fossil fuels, with households emitting around 4.8 tonnes of CO₂ annually. Recent government initiatives in energy efficiency and renewable integration have begun to moderate these trends, although challenges remain for sustainable urban development. Unique Contribution to Theory, Practice and Policy: Ecological modernization theory (EMT), social practice theory &amp; diffusion of innovations theory may be used to anchor future studies on the analyze energy consumption patterns and residential carbon footprint in Turkey. Practitioners should leverage state-of-the-art monitoring technologies such as smart meters and IoT devices to capture detailed data on residential energy use. Policymakers must design incentive schemes, such as tax rebates and subsidies, to encourage the adoption of renewable energy and energy-efficient retrofits in residential areas.

Study on Life-Cycle Carbon Emission of Urban Residential Buildings: A Case Study of Xi’an

According to statistics from the United Nations Environment Program (UNEP), the construction industry accounts for approximately 30% to 40% of global energy consumption and greenhouse gas emissions, making it a major source of carbon emissions. As a critical component of urban construction, residential buildings are characterized by their large scale and significant potential for carbon reduction. Building on this context, this study utilizes diversified geospatial data and applies the life-cycle stage framework for residential buildings alongside the emission factor method to calculate total carbon emissions during the material production, construction, and operation phases. It systematically analyzes the distribution characteristics and spatial evolution trends of life-cycle carbon emissions for urban residential buildings. The findings reveal that 63.06% of the cumulative carbon emissions from residential buildings in Xi’an originate from the operation phase, underscoring the importance of optimizing carbon emissions in this phase as a critical priority for future reductions. Additionally, the spatial distribution of residential building carbon emissions exhibits significant clustering, with an increasingly pronounced expansion pattern. Over time, the direction of expansion has shifted from a northeast–southwest orientation to a northwest–southeast trajectory and continues to extend toward peripheral areas. Economic growth, increased urbanization, and the intensive consumption of specific building materials are identified as significant drivers of residential carbon emissions, while population growth and improvements in material utilization efficiency help mitigate emissions to some extent. This study offers valuable insights to support the green development of China’s construction industry and to advance energy-saving and carbon-reduction strategies.

DOES PURCHASING ENERGY-EFFICIENT AIR CONDITIONERS REDUCE HOUSEHOLD ELECTRICITY CONSUMPTION? EVIDENCE FROM CHINA

Energy efficiency label policy has been implemented worldwide as a means to address the rise in residential carbon emissions resulting from the growing quantity and intensity of air conditioner (AC) usage triggered by climate change. Using data from the China General Social Survey, this study explored whether households that purchased energy-efficient AC (with energy efficiency labels) consumed less electricity and its mechanisms. We find that households purchasing energy-efficient AC consume 17.2% more electricity annually and 21.9% more during the summer compared to those who do not. This effect is particularly pronounced for younger households and those in warmer or hotter regions, but is constrained by older households and those with awareness of energy saving. The reasons are that the purchase of energy-efficient AC changes households’ AC usage behavior under climate change, including increased usage times, lower usage temperatures, and preferred standby settings. The findings above provide useful recommendations for policy formulation in collaboration with energy efficiency labels to reduce carbon emissions while ensuring human welfare under climate change.

Effects of Relative Humidity on Time-Resolved Molecular Characterization of Secondary Organic Aerosols from the OH-Initiated Oxidation of Cresol in the Presence of NOx.

While biomass burning (BB) is the largest source of fine particles in the atmosphere, the influence of relative humidity (RH) and photochemistry on BB secondary organic aerosol (BB-SOA) formation and aging remains poorly constrained. These effects need to be addressed to better capture and comprehend the evolution of BB-SOA in the atmosphere. Cresol (C7H8O) is used as a BB proxy to investigate these effects. It is emitted directly from BB and has been identified as a significant SOA precursor from residential wood-burning emissions. The gas- and particle-phase signal intensities are investigated using online mass spectrometers. An increase in the SOA mass yield of 7% is observed when the RH rises from 0.5-20 to 70-87%. At elevated RH, a significant increase in the formation of nitrogen-containing compounds is observed due to particle-phase processes. This is linked to a net decrease in the SOA viscosity, enabling these compounds to be formed to a greater extent at elevated RH in the presence of nitrogen oxides. These results highlight the importance of the particle water content for the molecular formation and aging of BB-SOA compounds.

Industrial Air Emissions and Breast Cancer Incidence in a United States-wide Prospective Cohort.

We evaluated air emissions of industrial compounds, many of which have carcinogenic or endocrine-disrupting properties, in relation to breast cancer incidence. Using the United States Environmental Protection Agency's Toxics Release Inventory, we quantified air emissions of 28 compounds near Sister Study participants' residences during the 10 years leading up to study enrollment (2003-2006; n = 46,150). We used Cox proportional hazards regression to estimate adjusted hazard ratios (HR) and 95% confidence intervals (CI) for associations of residential emission levels of single pollutants with incident breast cancer. We assessed pollutant mixtures using an exposure continuum mapping (ECM) framework and characterized associations using a joint-exposure response function. During follow-up (median = 13.4 years), we identified 4155 breast cancer cases. We observed nonmonotonic but elevated associations with breast cancer for emissions within 3 km of the residence for nickel compounds (HR quintile5vs.none = 1.3; 95% CI = 1.0, 1.6) and trichloroethylene (HR quintile5vs.none = 1.3; 95% CI = 1.0, 1.6). ECM identified 25 mixture profiles that explained 72% of the variance in emissions patterns, with most participants experiencing relatively low emissions profiles. The joint-exposure response function suggested that a higher incidence of breast cancer occurred among individuals with relatively rare, high emissions profiles; however, the overall trend was not associated with breast cancer ( P = 0.09). In our study, breast cancer incidence was associated with air emissions of certain industrial carcinogens. Although the overall emissions mixture did not show a trend related to breast cancer, this may not reflect the importance of individual compounds or specific emissions sources.

Peak Assessment and Driving Factor Analysis of Residential Building Carbon Emissions in China’s Urban Agglomerations

Urban agglomerations, as hubs of population, economic activity, and energy consumption, significantly contribute to greenhouse gas emissions. The interconnected infrastructure, energy networks, and shared economic systems of these regions create complex emission dynamics that cannot be effectively managed through isolated city-level strategies. However, these regions also present unique opportunities for innovation, policy implementation, and resource optimization, making them crucial focal points in efforts to reduce carbon emissions. This study examines China’s three major urban agglomerations: the Yangtze River Delta, the Pearl River Delta, and the Beijing–Tianjin–Hebei region. Utilizing data from 2005 to 2020 and a comprehensive evaluation model (BCPCAM), the research offers more profound insights into the socio-economic factors and collaborative mechanisms influencing emission trends, facilitating the development of targeted strategies for sustainable development and carbon neutrality. The findings indicate that (1) economic development and carbon control can progress synergistically to some extent, as economically advanced cities like Beijing and Shanghai have achieved their carbon peaks earlier; (2) natural resource endowment significantly affects urban carbon emissions, with resource-rich cities such as Tangshan and Handan, where fossil fuels dominate the energy mix, facing considerable challenges in reducing emissions; and (3) notable differences exist in the growth patterns of carbon emissions between urban and rural buildings, underscoring the need for tailored carbon reduction policies.

Atmospheric pah concentrations in a semi-urban site: temporal variation, risk assessment, source identification and estimation of levels in diverse environments.

This study evaluates atmospheric polycyclic aromatic hydrocarbon (PAH) concentrations in a semi-urban area, Görükle, Turkey, from June 2021 to February 2022. The average concentration of ∑16 PAHs was 24.85 ± 19.16 ng/m3, ranging from 6.70 to 59.11 ng/m3. Seasonal variations were observed, with winter concentrations approximately five times higher than those in summer, driven by increased residential heating emissions and adverse meteorological effects. The results were compared to literature values, revealing lower PAH levels than those found in other urban and industrial areas. Meteorological factors were statistically analyzed. PAH concentrations exhibited an inverse relationship with temperature and mixing height, while relative humidity showed a positive linear correlation. The study also estimated PAH concentrations in various environmental media using gas-particle partitioning and equilibrium models. The concentrations of PAHs in the particulate phase were minimal, suggesting that most PAHs were present in the gas phase. Additionally, the equilibrium concentrations of PAHs in soil, leaves, bark, and water were modeled, emphasizing the accumulation of heavier PAHs in these media yet 2- and 3-ring PAHs were predominant in the ambient air samples. Source apportionment and PCA studies identified vehicle emissions and combustion for heating purposes as the primary sources of pollution. However, air mass trajectory analysis indicated contributions from Kocaeli, an area with significant industrial activity. The cancer risk assessment yielded low risks for both adults and children.

Trends in the sizes and carbonaceous fractions of primary emitted particulate matter in China from 1960 to 2019.

The health impacts of particulate matter (PM) depend on its concentration, size and composition. Herein, we quantified the changes in the emissions of primary PM2.5, PM2.5-10 and PM>10 with aerodynamic diameters of<2.5μm, 2.5-10μm and >10μm, respectively, black carbon (BC), and organic carbon (OC) to address the changes and driving factors. The temporal trends of PM emissions follow Kuznets curves, with 1995 as the peak year when the gross domestic product per capita was only US$1023, showing a late-mover advantage. The fractions of PM2.5 : PM2.5-10 : PM>10 and BC : OC : non-carbonaceous-PM2.5 from various sectors varied following different trajectories. The mass fractions of PM2.5 : PM2.5-10 : PM>10 from iron-steel production industries changed from 21% : 12% : 67% in 1960 to 50% : 13% : 37% in 2019, showing a decrease in PM size. The fractions of BC were linearly correlated with PM2.5, whereas the dependence of OC on PM2.5 differed before and after 1995, owing to changes in residential emissions. Various factors influencing the changes in size and carbonaceous fraction were explored. The major factors were the promotion of dust-removal capacity and the transition in residential energy from solid fuels to emission-free fuels, which increased the fractions of fine PM and carbonaceous fraction.

Relationship between green bonds and carbon neutrality: evidence from top five emitting countries’ sectoral CO2 emissions

This study analyzes the influence of green bonds on carbon neutrality. It examines the daily data of sectoral CO2 emissions of the top five CO2-emitting nations from January 2, 2019 to December 30, 2022 using wavelet transform coherence, quantile-on-quantile regression, Granger causality in quantiles, and quantile regression approaches. The results revealed that (i) green bonds are strongly related to sectoral CO2 emissions; (ii) green bonds reduce transport sector CO2 emissions in China, the US, and Japan while causing an upsurge in India and Russia; (iii) green bonds reduce industrial sector CO2 emissions only in the US; (iv) green bonds have a declining influence in energy sector CO2 emissions at lower quantiles in India, China, and the US, whereas the impact increases at higher quantiles; and (v) green bonds decrease residential sector CO2 emissions in the US, Russia, and Japan. The study revealed that green bonds help reduce CO2 emissions in the residential sector in various quantiles. Therefore, the US, Russia, and Japan should raise household awareness of green energy utilization by promoting them with green bonds. In addition, green bonds can effectively reduce transportation sector CO2 emissions in China and the US. Therefore, the policymakers of the two global powers should contribute to global CO2 reduction by promoting green transportation and clean energy transition in the transportation sector through green bonds. Thus, green bonds can play an effective role in the fight against global warming.

Multiyear high-temporal-resolution measurements of submicron aerosols at 13 French urban sites: data processing and chemical composition

Abstract. This paper presents a first comprehensive analysis of long-term measurements of atmospheric aerosol components from aerosol chemical speciation monitor (ACSM) and multiwavelength Aethalometer (AE33) instruments collected between 2015 and 2021 at 13 (sub)urban sites as part of the French CARA (Chemical Characterization of Particles) program. The datasets contain the mass concentrations of major chemical species within submicron aerosols (PM1), namely organic aerosols (OAs), nitrate (NO3-), ammonium (NH4+), sulfate (SO42-), non-sea-salt chloride (Cl−), and equivalent black carbon (eBC). Rigorous quality control, technical validation, and environmental evaluation processes were applied, adhering to both guidance from the French Reference Laboratory for Air Quality Monitoring (LCSQA) and the Aerosol, Clouds, and Trace Gases Research Infrastructure (ACTRIS) standard operating procedures. Key findings include geographical differences in the aerosol chemical composition, seasonal variations, and diel patterns, which are influenced by meteorological conditions, anthropogenic activities, and proximity to emission sources. Overall, OA dominates PM1 at each site (43 %–60 % of total mass), showing distinct seasonality with higher concentrations (i) in winter, due to enhanced residential heating emissions, and (ii) in summer, due to increased photochemistry favoring secondary aerosol formation. NO3 is the second most important contributor to PM1 (15 %–30 %), peaking in late winter and early spring, especially in northern France, and playing a significant role during pollution episodes. SO4 (8 %–14 %) and eBC (5 %–11 %) complement the major fine-aerosol species, with their relative contributions strongly influenced by the origin of air masses and the stability of meteorological conditions, respectively. A comparison with the 3D chemical transport model (CTM) CHIMERE shows high correlations between simulations and measurements, albeit with an OA concentration underestimation of 46 %–76 %. Regional discrepancies in NO3 concentration levels emphasize the importance of these datasets with respect to validating air quality models and tailoring air pollution mitigation strategies. The datasets can be found at https://doi.org/10.5281/zenodo.13318298 (Chebaicheb et al., 2024).

An Enhanced Particle Swarm Optimization Long Short-Term Memory Network Hybrid Model for Predicting Residential Daily CO2 Emissions

This study aims to establish an accurate hybrid model for predicting residential daily carbon dioxide (CO2) emissions, offering essential theoretical insights and data support for decision-makers in the construction industry. A hybrid model named CRLPSO-LSTM was proposed, which integrates an enhanced particle swarm optimization (CRLPSO) algorithm with a long short-term memory (LSTM) network. The CRLPSO algorithm enhances population quality, diversity, and global search efficiency by introducing improved circle chaotic mapping, optimizing worst mutations, and incorporating the Lévy flight strategy. The performance of the CRLPSO algorithm was rigorously evaluated using 23 internationally recognized standard test functions. Subsequently, the CRLPSO algorithm was employed to optimize the parameters of the LSTM model. Experimental validation was performed on three datasets from China, the United States, and Russia, each exhibiting distinct emissions characteristics: China with high emissions and high volatility, the United States with medium emissions and medium volatility, and Russia with low emissions and low volatility. The results indicate that the CRLPSO-LSTM hybrid model outperformed other hybrid models in predicting residential daily CO2 emissions, as demonstrated by superior R2, MAE, and MSE metrics. This study underscores the effectiveness and broad applicability of the CRLPSO-LSTM hybrid model, offering a robust theoretical foundation and data support for advancing the sustainable development goals.