Air Pollutant Emissions Research Articles

High-resolution dispersion modelling of PM2.5, PM10, NOx and NO2 exposure in metropolitan areas in Sweden 2000‒2018 – large health gains due to decreased population exposure

Ambient air pollution remains the major environmental cause of disease. Accurate assessment of population exposure and small-scale spatial exposure variations over long time periods is essential for epidemiological studies. We estimated annual exposure to fine and coarse particulate matter (PM2.5, PM10), and nitrogen oxides (NOx, NO2) with high spatial resolution to examine time trends 2000‒2018, compliance with the WHO Air Quality Guidelines, and assess the health impact. The modelling area covered six metropolitan areas in Sweden with a combined population of 5.5 million. Long-range transported air pollutants were modelled using a chemical transport model with bias correction, and locally emitted air pollutants using source-specific Gaussian-type dispersion models at resolutions up to 50 × 50 m. The modelled concentrations were validated using quality-controlled monitoring data. Lastly, we estimated the reduction in mortality associated with the decrease in population exposure. The validity of modelled air pollutant concentrations was good (R2 for PM2.5 0.84, PM10 0.61, and NOx 0.87). Air pollution exposure decreased substantially, from a population weighted mean exposure to PM2.5 of 12.2 µg m−3 in 2000 to 5.4 µg m−3 in 2018. We estimated that the decreased exposure was associated with a reduction of 2719 (95% CI 2046–3055) premature deaths annually. However, in 2018, 65%, 8%, and 42% of residents in the modelled areas were still exposed to PM2.5, PM10, or NO2 levels, respectively, that exceeded the current WHO Air Quality Guidelines for annual average exposure. This emphasises the potential public health benefits of reductions in air pollution emissions.

Distributional impacts of fleet-wide change in light duty transportation: mortality risks of PM2.5 emissions from electric vehicles and Tier 3 conventional vehicles

Abstract Light-duty transportation continues to be a significant source of air pollutants that cause premature mortality and greenhouse gases (GHGs) that lead to climate change. We assess PM2.5 emissions and its health consequences under a large-scale shift to electric vehicles (EVs) or Tier-3 internal combustion vehicles (ICVs) across the United States, focusing on implications by states and for the fifty most populous metropolitan statistical areas (MSA). We find that both Tier-3 ICVs and EVs reduce premature mortality by 80%–93% compared to the current light-duty vehicle fleet. The health and climate mitigation benefits of electrification are larger in the West and Northeast. As the grid decarbonizes further, EVs will yield even higher benefits from reduced air pollution and GHG emissions than gasoline vehicles. EVs lead to lower health damages in almost all the 50 most populous MSA than Tier-3 ICVs. Distributional analysis suggests that relying on the current gasoline fleet or moving to Tier-3 ICVs would impact people of color more than White Americans across all states, levels of urbanization, and household income, suggesting that vehicle electrification is more suited to reduce health disparities. We also simulate EVs under a future cleaner electric grid by assuming that the 50 power plants across the nation that have the highest amount of annual SO2 emissions are retired or retrofitted with carbon capture and storage, finding that in that case, vehicle electrification becomes the best strategy for reducing health damages from air pollution across all states.

Heterogeneities in Regional Air Pollutant Emission Mitigation Across China During 2012–2020

AbstractThe toughest‐ever clean air actions to date in China have helped to reduce national air pollutant emissions significantly in recent years. However, the heterogeneous mitigation paths and their determinated factors among regions were less concerned. To direct regional mitigation strategies more efficiently, we compiled a time‐series emission inventory of Chinese 30 provinces for SO2, NOx, primary PM2.5 and CO2 at detailed socioeconomic sectors, and then used spatial‐temporal index decomposition analysis to evaluate the driving forces for changes in regional pollutant emissions and emission intensities from 2012 to 2017 and from 2017 to 2020, separately. Our results showed that end‐of‐pipe control measures dominated the decreasing trend of China's pollutant emissions (38%–68% for SO2, NOx, and primary PM2.5), but their relative dominance diminished gradually, and the effects from energy policy emerged, even outweighing the former in southern areas during the second phase. Though driven by different factors, the chemistry, nonmetal, metal, energy and residential sectors dominated regional reduction trend and with varying degrees. Clean air action led to the convergence of regional emission intensity, while optimizing and upgrading industrial structure and improving energy efficiency is still necessary for further reduction in northern and western areas at present.

SUSTAINABLE URBAN MOBILITY PLAN AS ONE OF THE TOOLS OF MANAGEMENT OF URBAN TRANSPORT SYSTEMS

The concepts of "urban mobility" and "sustainable urban mobility plan" are defined. Indicators of sustainable transport planning problems in various categories (trip activity, air pollution emissions, noise pollution, transport risk, economic productivity, general accessibility, land use impact, equity capital, transport policy) are also given, possible sub-categories are proposed and the evaluation of indicators according to the principle: A = suggested for use in almost all situations; B = proposed for use if appropriate/possible; C = proposed for use when appropriate to address specific community needs. A number of factors that should be paid attention to in the process of creating a sustainable urban mobility plan are described. It also describes the main stages of creating a plan and the features that should be considered during the planning process in order to successfully implement sharing systems in a mobility strategy.

Air pollutant emissions caused by receiving international industrial transfer in Southeast Asian developing countries from 1990 to 2018

Receiving international industrial transfer (mainly foreign direct investment, FDI) is extremely important for economic development but also brings negative environmental impacts for Southeast Asian developing countries (SEADCs). Due to relatively low labor costs and large market potential, SEADCs have become an attractive destination for industrial transfer after China, while studies were far from sufficient on the associated air pollutant emissions that would worsen air quality and threaten human health. We develop an exploratory framework to estimate the long-term trends of relevant air pollutant emissions in eight major SEADCs, including Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Thailand, and Vietnam. During 1990–2018, the emissions generally show a fluctuating upward trend and increased significantly in Cambodia, Laos, Philippines, and Vietnam. The total emissions of CO, NMVOC, SO2, NOX, PM2.5, and NH3 from the eight SEADCs increased from 19.0, 4.3, 3.6, 1.5, 0.5, and 0.4 kilotons (kt) to 391.6, 260.9, 271.1, 182.4, 48.4, and 12.2 kt, respectively. The emission growth in almost all SEADCs accelerated after 2008 and faster than FDI growth. The disparities in emissions among SEADCs basically grew first and then declined to a level lower than that of 1990, but generally exceeded the disparities in FDI. Productivity gain and emission intensity decrease primarily caused the emission growth and reduction, respectively. Relatively small reductions in emission intensity are found for NOX and SO2. In general, most SEADCs have utilized FDI for economic development without sufficient efforts on air pollutant emission controls. Our outcomes can inform the formulation and optimization of relevant policies reconciling economic development and air quality improvement in SEADCs.

Assessing climate/air quality synergies and cost-effectiveness for Beijing transportation: Insights into sustainable development

CO2 emission reduction policies can simultaneously reduce air pollutant emissions in the transport sector, but the extent of their cost-effective is under-evaluated. To explore sustainable transportation in a mega-city, the potential synergies and cost-effectiveness of CO2 and air pollutant emission reductions in Beijing transportation are quantitatively evaluated by radar chart analysis, total cost of ownership (TCO) and marginal abatement cost curves (MACC) under various mitigation measures. The findings show that the adoption of electric light-duty vehicles can achieve a significant synergistic effect and cost-effectiveness of emission reductions, and would be implemented as a high priority. Moreover, improving emission standards and fuel economy have an obvious cost-effectiveness and effective mitigation for air pollutants, but with poor synergies due to low CO2 mitigation. In contrast, the clean energy for large and heavy vehicles, and bio-fuel for aviation are essential measures for achieving carbon neutrality, but with high costs. Furthermore, changes of transport modes have good cost-effectiveness, but there are no synergies due to the emission increments of PM2.5 and NOx. Given that transportation plays a crucial role in achieving carbon neutrality and enhancing air quality, more stringent and effective environmental policies targeting emission reduction can expedite the sustainable transition in Beijing transportation.

Prediction Analysis of the Number of Patients with Respiratory Diseases based on SVR

The rapid development of industrialization is accompanied by a further increase in air pollution. Serious air pollution will bring a variety of diseases to the human body, which has been a basic consensus on a global scale. It is of great significance to further explore the specific diseases related to air pollution. In this paper, air pollutant emission data and respiratory disease data of public data sets are collected to analyze the changes between them and the relationship between them. Further, the SVR machine learning analysis model was established to analyze the impact of air pollutant emission on the number of patients with respiratory system diseases. Meanwhile, the data of the number of patients with respiratory system diseases was predicted based on the data of air pollutant emission, and the experimental effect was satisfactory. It lays a foundation for further research on the relationship between various air pollutants and human respiratory diseases.

A Three-Wavelength Optical Sensor for Measuring the Multi-Particle-Size Channel Mass Concentration of Thermal Power Plant Emissions.

Emissions from thermal power plants have always been the central consideration for environmental protection. Existing optical sensors in thermal power plants usually measure the total mass concentration of the particulate matter (PM) by a single-wavelength laser, bearing intrinsic errors owing to the variation in particle size distribution (PSD). However, the total mass concentration alone cannot characterize all the harmful effects of the air pollution caused by the power plant. Therefore, it is necessary to measure the mass concentration and PSD simultaneously, based on which we can obtain multi-particle-size channel mass concentration. To achieve this, we designed an optical sensor based on the three-wavelength technique and tested its performance in a practical environment. Results showed that the prototype cannot only correctly measure the mass concentration of the emitted PM but also determine the mean diameter and standard deviation of the PSDs. Hence, the mass concentrations of PM10, PM2.5, and PM1 are calculated, and the air pollutants emission by a thermal power plant can be estimated comprehensively.

Addressing building related energy burden, air pollution, and carbon emissions of a low-income community in Southern California

This study examines the impact of low-income assistance and electrification programs on a disadvantaged community in Southern California. An urban building energy model is paired with an AC power flow and electric distribution system degradation model to evaluate how the cost of energy, carbon emissions, and pollutant emissions change after applying building weatherization, energy efficiency, and electrification measures to the community. Results show that traditional weatherization and energy efficiency measures (upgrading lighting and appliances, improving insulation to current building code standards) are the most cost-effective, reducing the cost of energy and carbon emissions by 10–20 % for the current community. Heat pump water heaters offer a 40 % average reduction in carbon emissions and almost 50 % decrease in criteria pollutant emissions, but at a cost increase of 17–22 %. Appliance electrification also reduces carbon emissions 5–10 % but increases cost by 7 % to 25 %. For reducing carbon, government programs that support building electrification are most cost-effective when they combine switching from natural gas to electricity with high efficiency system. Electrifying hot water and appliances effectively reduces emissions but must be paired with improved low-income assistance programs to prevent increased energy burden for low-income families. The urban building energy model and electrical distribution simulations used in this study can be replicated in other low-income communities.

The Potential of Using SDGSAT-1 TIS Data to Identify Industrial Heat Sources in the Beijing–Tianjin–Hebei Region

It is crucial to detect and classify industrial heat sources for sustainable industrial development. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data were first introduced for detecting industrial heat source production areas to address the difficulty in identifying factories with low combustion temperatures and small scales. In this study, a new industrial heat source identification and classification model using SDGSAT-1 TIS and Landsat 8/9 Operational Land Imager (OLI) data was proposed to improve the accuracy and granularity of industrial heat source recognition. First, multiple features (thermal and optical features) were extracted using SDGSAT-1 TIS and Landsat 8/9 OLI data. Second, an industrial heat source identification model based on a support vector machine (SVM) and multiple features was constructed. Then, industrial heat sources were generated and verified based on the topological correlation between the identification results of the production areas and Google Earth images. Finally, the industrial heat sources were classified into six categories based on point-of-interest (POI) data. The new model was applied to the Beijing–Tianjin–Hebei (BTH) region of China. The results showed the following: (1) Multiple features enhance the differentiation and identification accuracy between industrial heat source production areas and the background. (2) Compared to active-fire-point (ACF) data (375 m) and Landsat 8/9 thermal infrared sensor (TIRS) data (100 m), nighttime SDGSAT-1 TIS data (30 m) facilitate the more accurate detection of industrial heat source production areas. (3) Greater than 2~6 times more industrial heat sources were detected in the BTH region using our model than were reported by Ma and Liu. Some industrial heat sources with low heat emissions and small areas (53 thermal power plants) were detected for the first time using TIS data. (4) The production areas of cement plants exhibited the highest brightness temperatures, reaching 301.78 K, while thermal power plants exhibited the lowest brightness temperatures, averaging 277.31 K. The production areas and operational statuses of factories could be more accurately identified and monitored with the proposed approach than with previous methods. A new way to estimate the thermal and air pollution emissions of industrial enterprises is presented.

Diesel to Solar Irrigation System: Economic, Environmental, and Social Acceptability Analyses by Small-Scale Farmers of Calapan, Oriental Mindoro

Solar irrigation systems are sustainable practices that can improve the well-being of local communities and enhance the resilience of agriculture to climate change while reducing environmental impacts. Due to its high investment cost, small-scale farmers are inclined to use traditional fossil-based irrigation systems that can harm humans and the environment. This study aims to analyze the environmental impacts, economic feasibility, and social acceptability of shifting agricultural practices from diesel-fueled to solar irrigation systems. Taking the perspective of small-scale farmers from Calapan City, Oriental Mindoro, results found that solar irrigation system has a higher initial investment but lower maintenance and operational costs. These resulted in an attractive economic feasibility of the project with Php 19,693 of fuel cost savings per hectare per year, a project net present value of Php 10,214 per hectare, a payback period of 8.27 years, and returns on investment at 110%. Additionally, shifting to a solar irrigation system significantly reduces the greenhouse gas emissions from diesel at 199.78 CO2 eq/ha/yr, and avoids air pollutant emissions at 14.91 g/ha/yr particulate matter, 2.98 g/ha/yr nitrogen oxides,193.82 g/ha/yr sulfur oxides, and 149.09 g/ha/yr carbon monoxide. Despite the lack of in-depth environmental awareness, small-scale farmers are interested in investing in solar irrigation systems with 68% social acceptability. Results provide bases for recommendations on promoting more human ecologically and sustainable agriculture irrigation systems in the Philippines and other developing countries.

The Coordinated Effects of CO2 and Air Pollutant Emission Changes Induced by Inter-Provincial Trade in China

Inter-provincial trade leads to changes in CO2 and air pollutant emissions. However, there is a research gap regarding the coordinated effects (co-effects) between embodied CO2 and air pollutant emissions in trade. Understanding co-effects in inter-provincial trade is a prerequisite for driving the green transformation of trade and achieving coordination between pollution and carbon reduction. Here, we calculated provincial-level CO2 and air pollutant emission leakage in 2012 and 2017 based on a modified input–output model and, for the first time, investigated the co-effects between CO2 and air pollutant emission leakage caused by emissions transfers in China. Three types of co-effects, categorized as co-benefits, trade-offs, and co-damage, were discovered and defined to reveal the provincial differences. Furthermore, combined with structural decomposition analysis (SDA), we calculated the interannual variation in trade-induced emissions and identified the key driving factors of provincial-level co-effects from 2012 to 2017. Optimizing the energy structure has led to the greatest co-benefits, while changes in the industrial structure and emission coefficients have led to limited co-benefits in specific provinces. Variations in trade volume have led to co-damages across all provinces, and changes in emission coefficients have led to trade-offs in the majority of provinces. The case analysis confirmed that identifying and adjusting the key driving factors of co-effects can promote the transformation from co-damage and trade-offs to co-benefits. The findings implied a new approach for the reduction in pollution and carbon through inter-provincial trade.

Electrification of Transit Buses in the United States Reduces Greenhouse Gas Emissions.

The transportation sector is the largest emitter of greenhouse gas emissions (GHGs) in the United States. Increased use of public transit and electrification of public transit could help reduce these emissions. The electrification of public transit systems could also reduce air pollutant emissions in densely populated areas, where air pollution disproportionally burdens vulnerable communities with high health impacts and associated social costs. We analyze the life cycle emissions of transit buses powered by electricity, diesel, gasoline, and compressed natural gas and model GHGs and air pollutants mitigated for a transition to a fully electric U.S. public transit bus fleet using transit agency-level data. The electrification of the U.S. bus fleet would reduce several conventional air pollutants and has the potential to reduce transit bus GHGs by 33-65% within the next 14 years depending on how quickly the transition is made and how quickly the electricity grid decarbonizes. A levelized cost of driving analysis shows that with falling capital costs and an increase in annual passenger-kilometers of battery electric buses, the technology could reach levelized cost parity with diesel buses when electric bus capital costs fall below about $670 000 per bus.

The effect of air pollution on the risk of inflammatory bowel disease, colorectal cancer and appendicitis

Introduction and objective
 Intense industrial development is contributing to increased emissions of air pollutants. Numerous studies indicate a link between air pollution and a higher risk of pulmonary and cardiovascular diseases. Our goal is to analyze available scientific studies to demonstrate the relationship between air pollution and the risk of developing and complicating inflammatory bowel disease, colorectal cancer and appendicitis.
 Brief description of the state of knowledge
 Air pollution is a global health challenge. Particles such as PM, nitrogen oxides, sulfur oxides or ozone have a significant impact on health. Studies suggest a link between pollution and intestinal diseases (IBD, CRC, appendicitis). Air pollutants can affect the gut by causing inflammation, oxidative stress and disruption of the gut microbiota. Exposure to PM2.5 can increase the risk of IBD, CRC, and PM10 can increase the risk of UC. There is a correlation between pollution and the rate of hospitalization for IBD. In addition, exposure to PM2.5 increases the risk of colorectal cancer mortality. Short-term exposure to air pollution is associated with more hospitalizations for appendicitis.
 Summary
 Air pollution is a real health risk. Reducing air pollution could potentially reduce the risk of developing these intestinal diseases and their complications. Despite the potential links, further research is required to better understand the mechanisms of action of pollutants on intestinal health. Urgent and coordinated global and local action is needed to reduce air pollution emissions and protect the health of the public.

Evaluation of exhaust emissions of agricultural tractors using portable emissions measurement system in Korean paddy field

Recently, diesel engine emissions have been designated as a first-class carcinogen by the World Health Organization (WHO). As such, problems with diesel engine emissions continue to increase around the world. This study aimed to analyze the emissions (CO, NOx, PM) of agricultural tractors during farming operations in order to build a reliable national inventory of air pollutant emissions. Emission data were collected using a portable emission measurement system during actual agricultural operation. The load factor (LF) of the engine was calculated using the collected engine information, the emission factor was analyzed using the LF and the measured emission. The LF was significantly different from the current standard value of 0.48, which is used in Korea to calculate exhaust emissions. The deviation ratio of the emission factor was 0.039 ~ 56.59 compared to Tier-4 emission regulation standards. Under many conditions, the calculated emission factor was higher than the emission limit. Thus, this study provides useful information for emission inventory construction through emission calculation under actual conditions and suggests the need to realize the currently applied emission factor.

Development of a greenhouse gas - air pollution interactions and synergies model for Korea (GAINS-Korea)

This study aimed to create Greenhouse Gas - Air Pollution Interactions and Synergies (GAINS)-Korea, an integrated model for evaluating climate and air quality policies in Korea, modeled after the international GAINS model. GAINS-Korea incorporates specific Korean data and enhances granularity for enabling local government-level analysis. The model includes source-receptor matrices used to simulate pollutant dispersion in Korea, generated through CAMx air quality modeling. GAINS-Korea's performance was evaluated by examining different scenarios for South Korea. The business as usual scenario projected emissions from 2010 to 2030, while the air quality scenario included policies to reduce air pollutants in line with air quality and greenhouse gas control plans. The maximum feasible reduction scenario incorporated more aggressive reduction technologies along with air quality measures. The developed model enabled the assessment of emission reduction effects by both greenhouse gas and air pollutant emission reduction policies across 17 local governments in Korea, including changes in PM2.5 (particulate matter less than 2.5 μm) concentration and associated benefits, such as reduced premature deaths. The model also provides a range of visualization tools for comparative analysis among different scenarios, making it a valuable resource for policy planning and evaluation, and supporting decision-making processes.

Microalgae cultivation using ammonia and carbon dioxide concentrations typical of pig barns

ABSTRACT While global population growth drives increased production efficiency in animal agriculture, there is a growing demand for environmentally friendly practices, particularly in reducing air pollutant emissions from concentrated animal feeding operations. This study explores the potential of cultivating microalgae in photobioreactors (PBRs) as an eco-friendly and cost-effective approach to mitigate NH3 and CO2 emissions from pig barns. Unlike traditional physicochemical mitigation systems, microalgae offer a renewable solution by converting pollutants into valuable biomass. The research focused on Scenedesmus dimorphus growth under typical NH3 and CO2 concentrations found in the indoor air of pig barns. Four NH3 (0, 12, 25, and 50 ppm) and four CO2 concentrations (350, 1200, 2350, and 3500 ppm) were tested using photobioreactors. Results showed a maximum specific growth rate of 0.83 d−1 with 12 ppm NH3 and 3500 ppm CO2. The dry biomass concentration was significantly higher (1.16 ± 0.08 g L−1; p < 0.01) at 25 ppm NH3 and 2350 ppm CO2 than other test conditions. S. dimorphus demonstrated the peak NH3 and CO2 fixation rates (23.8 ± 2.26 mg NH3 L−1 d−1 and 432.24 ± 41.09 mg CO2 L−1 d−1) at 25 ppm NH3 and 2350 ppm CO2. These findings support the feasibility of using algae to effectively remove air pollutants in pig barns, thereby improving indoor air quality.

Unveiling the Spatial-Temporal Characteristics and Driving Factors of Greenhouse Gases and Atmospheric Pollutants Emissions of Energy Consumption in Shandong Province, China

As the largest energy-consuming province in China, Shandong faces the dual task of greenhouse gas (GHG) reduction and atmospheric pollution control. Based on the latest activity data and updated emission factors, this study establishes a high-resolution emission inventory (5 km × 5 km) for GHGs and main atmospheric pollutants from the energy consumption sectors of Shandong Province from 2010 to 2021, quantifies the relationship between social economic factors and GHGs and atmospheric pollutants emissions using the expanded stochastic environmental impact assessment (STIRPAT) model, and forecasts the future emission trend with the help of the scenario analysis method. Results indicate that the electricity and transportation sectors are the main contributors to all pollutants. Spatially, the high value of pollutants is mainly concentrated in the urban agglomerations of central and eastern Shandong. Up to 72% of GHGs and 50% of air pollution emissions are attributed to the top 10% of emission grids. Emission peaks occur mainly in summer and winter due to straw burning, increased utilization of temperature-controlled facilities, and expansion of plant capacity. Population, energy consumption, the proportion of secondary industry, and energy consumption intensity are the most significant influencing factors for pollutant emissions. Scenario analysis results indicate Shandong province can reach its carbon peak in 2027 without sacrificing population growth or economic progress.

Long-Term Prevalence Trends in Children in Relation to Environmental Issues in the Astrakhan Region

Introduction: One of the tasks of preventive medicine and hygiene is to study the relationship between environmental factors and human health. The purpose of the study was to compare pediatric prevalence rates in the Astrakhan Region and in the city of Astrakhan in 2007–2020 with those for the Russian Federation in relation to per capita emissions of environmental pollutants. Materials and methods: We used official statistics on the size of population, prevalence rates in children, and emissions of air pollutants in the Astrakhan Region for the years under study. Methods of mathematical and statistical analysis, construction of trend lines and calculation of linear regression equations using built-in Microsoft Excel software were applied. An online Spearman’s Rho Calculator was used to compute correlation coefficients. Results: The prevalence in children showed a statistical downward trend both in Astrakhan and in the Astrakhan Region as a whole with its rates being, on average, 7.8 % and 15.6 % lower than the national ones, respectively. The Spearman’s Rho (rs) showed the presence of a high and direct statistically significant correlation between per capita emissions of air pollutants and disease prevalence in the child population in the Astrakhan Region (rs = 0.73; p = 0.003) with a moderate direct, yet insignificant, correlation for the Russian Federation as a whole (rs = 0.32; p = 0.41) and null correlation for the data computed for the of Astrakhan (rs = 0.26; p = 0.32). Conclusion: Along with the predicted decreasing trend in prevalence in the pediatric population, we established a statistical relationship between local environmental problems and children’s health, which should be taken into account in the development of disease prevention programs.

Does e-shopping impact household travel? Evidence from the 2017 U.S. NHTS

How does e-shopping impact household travel? To answer this question, which is particularly relevant for policymakers concerned with congestion, air pollution, and greenhouse gas emissions from transportation, we analyzed data from the 2017 National Household Travel Survey using propensity score matching. This allowed us to tackle the bias from households self-selecting into various levels of e-shopping and gain causal inference. Unlike other related papers in the literature, our unit of analysis is a household because travel and shopping decisions within a household are interrelated. We classified households into three groups based on how many orders per person per month they placed online: low (up to one), medium (more than once but less than four), and high (over four). We found that more e-shopping results in more household travel (number of trips, miles, and VMT), but this effect depends on e-shopping frequency and population density, and it affects weekdays more than weekends. E-shopping impacts household travel more for medium frequency e-shoppers in low density areas: compared to similar low frequency e-shoppers, on weekdays, they take on average 8 more monthly trips and travel ∼104 extra miles (including 31 miles for shopping). At the other end of the spectrum, high frequency e-shoppers in dense areas do not travel more on weekends than similar low e-shopping frequency households. To help reduce e-shopping induced travel, policymakers could encourage the creation of neighborhood depots where households would pick-up and return unwanted orders, and foster the development of virtual reality tools for shopping from home.