The 2015 Paris agreement aims to cut greenhouse gas emissions and keep global temperature rise below 2 °C above pre-industrial levels. Reducing CH₄ emissions from leaking pipelines presents a relatively achievable objective. While walking and driving surveys are commonly used to detect leaks, the detection probability (DP) is poorly characterized. This study aims to investigate how leak rates, survey distance and speed, and atmospheric conditions affect the DP in controlled belowground conditions with release rates of 0.5–8.5 g min⁻¹. Results show that DP is highly influenced by survey speed, atmospheric stability, and wind speed. The average DP in Pasquill–Gifford stability (PG) class A is 85% at a low survey speed (2–11 mph) and decreases to 68%, 63%, 65%, and 60% in PGSC B/C, D, E/F, and G respectively. It is generally less than 25% at a high survey speed (22–34 mph), regardless of stability conditions and leak rates. Using the measurement data, a validated DP model was further constructed and showed good performance (R2: 0.76). The options of modeled favorable weather conditions (i.e., PG stability class and wind speed) to have a high DP (e.g., >50%) are rapidly decreased with the increase in survey speed. Walking survey is applicable over a wider range of weather conditions, including PG stability class A to E/F and calm to medium winds (0–5 m s⁻¹). A driving survey at a low speed (11 mph) can only be conducted under calm to low wind speed conditions (0–3 m s⁻¹) to have an equivalent DP to a walking survey. Only calm wind conditions in PG A (0–1 m s⁻¹) are appropriate for a high driving speed (34 mph). These findings showed that driving survey providers need to optimize the survey schemes to achieve a DP equivalence to the traditional walking survey.

Air pollution is among the top risk faced by people around the world, and therefore combating it is among the top priorities. It begins with identifying the sources that contribute the most to local air pollution to prioritize their control. There are advanced methods for source identification and apportionment, but such methods are not available in many low-income countries and not everywhere in all high-income countries. We propose a simplified method by using source the signatures to help obtain information about the local source contribution if no other methods are available. Using low-cost monitors, particle mass (PM₂.₅) and carbon monoxide (CO) concentrations were measured and the ratio of CO/PM₂.₅ was determined. We investigated outdoor and indoor sources, including vehicular exhaust, combustion of biomass, incense and mosquito coil burning, and cigarette smoking. The results show that the ratios differed significantly between certain pollutant sources. Compressed natural gas (CNG) engines have a high ratio (mean value of 972 ± 419), which is attributed to relatively low PM₂.₅ emissions, while ship emissions and cigarette smoke recorded a relatively low ratio. Most traffic emissions recorded higher ratios than those of bushfire emissions, and ratios of most outdoor pollutant sources were much higher than those of indoor pollutant sources. There is a clear trend for ratios to decrease from high to low for CNG, petrol, diesel for buses, and fuel for ships. Our results suggest that the ratio of CO/PM₂.₅ can be used as an effective method to identify pollution sources.

The emission factor (EF) is a parameter used to assess vehicle emissions. Many studies have reported EFs for vehicles in driving conditions. However, the idling emissions should not be neglected in characterizing actual vehicle emissions in congested large cities, where idling is very common on the road. Whereas, EF data for idling vehicles have scarcely been reported in the literature, let alone comparison of different fuels. In this study, the EFs of passenger cars burning four types of fuels - gasoline, compressed natural gas (CNG), diesel, and liquefied petroleum gas (LPG) were measured and compared. The emissions data for CO, CO₂, unburned hydrocarbon (HC), and NO were recorded to calculate fuel-based EFs in units of g pollutants/kg fuel burned. EFs for CO, HC, and NO were compared for the four fuels. Diesel vehicles had the highest EF for CO, with an average value of 35.12 ± 21.37 g/kg fuel, due to low concentration of CO₂ in lean operation compared to CO emission. CNG vehicles had the highest EF for HC, with an average value of 28.15 ± 11.97 g/kg fuel, due to high concentration of unburned methane gas due to slow CNG flame speed whereas diesel vehicles again had the highest EF for NO due to high temperature and pressure and freezing of NO decomposition reaction, with an average value of 12.07 ± 5.37 g/kg fuel. Further comparison was conducted to analyze the effects of two additional variables on EF: engine displacement volume and model/brand year. Only the gasoline-fueled vehicles showed an increase in EFs (for CO, HC and NO) with the vehicle age according to the model year. However, no clear correlation was observed for CNG, LPG, and diesel-fueled vehicles. Finally, the EF results were compared with those reported in the literature, which have been measured in various countries under both idling and non-idling conditions. Because the idling EFs were not substantially smaller than those under moving conditions, and vehicles spend substantial time idling in large cities, idling emissions should not be ignored in the emission inventories for large cities.

Urban transportation is one of the leading causes of air pollution in big cities. In-use emissions of vehicles are higher than the emission control certification levels. The current study uses a roadside remote sensing emission monitoring campaign to investigate (a) fraction of high emitters in the light-duty vehicle (LDV) fleet and their contributions to the total emissions, (b) emission inspection (I/M) programs' effectiveness, and (c) alternate fuel (natural gas) encouragement policy. LDVs consist of passenger or freight transport vehicles with four wheels equivalent to classes M1 and N1 of European union vehicle classifications. The motivation is to assess the current emission inspection program's success rate and study the impact of the increased natural gas vehicle market share policy. It is also meant to present and validate remote sensing as a possible backup method to the current I/M program.The emission remote sensing campaign was conducted to measure emissions of CO, HC, and NO of the LDV fleet. Fleet age, engine size, and fuel type (gasoline or natural gas) were extracted and correlated with emissions. It was found that CO and HC emissions are five times higher for cars more than fifteen years old of age compared to those less than five years old. Analyses of high-emitters showed that almost 20% of the fleet were high-emitters and responsible for roughly half of CO, HC, and NO emissions.The correlation between the I/M program and the remote sensing to identify high-emitters was weak. Which indicates the need for an improved I/M program. It shows that even a limited remote sensing campaign is beneficial as a complementary monitoring tool to the I/M program. The study showed the same fraction of high-emitters in natural gas (methane) vehicles, despite the national policies to increase natural gas vehicle fraction in the market for reduced emissions.

This study presents the results of an integrated model developed to evaluate the environmental and health impacts of Electric Vehicle (EV) deployment in a large metropolitan area. The model combines a high-resolution chemical transport model with an emission inventory established with detailed transportation and power plant information, as well as a framework to characterize and monetize the health impacts. Our study is set in the Greater Toronto and Hamilton Area (GTHA) in Canada with bounding scenarios for 25% and 100% EV penetration rates. Our results indicate that even with the worst-case assumptions for EV electricity supply (100% natural gas), vehicle electrification can deliver substantial health benefits in the GTHA, equivalent to reductions of about 50 and 260 premature deaths per year for 25% and 100% EV penetration, compared to the base case scenario. If EVs are charged with renewable energy sources only, then electrifying all passenger vehicles can prevent 330 premature deaths per year, which is equivalent to $3.8 Billion (2016$CAD) in social benefits. When the benefit of EV deployment is normalized per vehicle, it is higher than most incentives provided by the government, indicating that EV incentives can generate high social benefits.

In China, the huge amounts of energy consumption caused severe carcinogenic polycyclic aromatic hydrocarbon (PAHs) concentration in the soil and ambient air. This paper summarized that the references published in 2008–2018 and suggested that biomass, coal and vehicular emissions were categorized as major sources of PAHs in China. In 2016, the emitted PAHs in China due to the incomplete combustion of fuel was about 32720 tonnes, and the contribution of the emission sources was the sequence: biomass combustion > residential coal combustion > vehicle > coke production > refine oil > power plant > natural gas combustion. The total amount of PAHs emission in China at 2016 was significantly decreased due to the decrease of the proportion of crop resides burning (indoor and open burning).The geographical distribution of PAHs concentration demonstrated that PAHs concentration in the urban soil is 0.092–4.733 μg/g. At 2008–2012, the serious PAHs concentration in the urban soil occurred in the eastern China, which was shifted to western China after 2012.The concentration of particulate and gaseous PAHs in China is 1–151 ng/m3 and 1.08–217 ng/m3, respectively. The concentration of particle-bound PAHs in the southwest and eastern region are lower than that in north and central region of China. The incremental lifetime cancer risk (ILCR) analysis demonstrates that ILCR in the soil and ambient air in China is below the acceptable cancer risk level of 10−6 recommended by US Environmental Protection Agency (EPA), which mean that there is a low potential PAHs carcinogenic risk for the soil and ambient air in China.

Twenty-six polycyclic aromatic hydrocarbons (PAHs) and four synthetic musk compounds (SMCs) accumulated by Masson pine needles from different areas of Shanghai were investigated in the present study. Concentrations of Σ26PAHs (sum of 26 PAHs) ranged from 234 × 10−3 to 5370 × 10−3 mg kg−1. Levels of Σ26PAHs in different sampling areas followed the order: urban areas (Puxi and Pudong) > suburbs > Chongming. Total concentrations of 16 USEPA priority PAHs ranged from 225 × 10−3 to 5180 × 10−3 mg kg−1, ranking at a relatively high level compared to other regions around the world. Factor analysis and multi-linear regression model has identified six sources of PAHs with relative contributions of 15.1% for F1 (vehicle emissions), 47.8% for F2 (natural gas and biomass combustion), 7.8% for F3 (oil), 10.6% for F4 (coal combustion), 15.7% for F5 (“anthracene” source) and 3.0% for F6 (coke tar). Total concentrations of 4 SMCs varied between 0.071 × 10−3 and 2.72 × 10−3 mg kg−1 in pine needles from Shanghai. SMCs with the highest detected frequency were Galaxolide and musk xylene, followed by musk ketone and Tonalide. The highest level of SMCs was found near industrial park and daily chemical plant. The results obtained from this study may have important reference value for local government in the control of atmospheric organic pollution.

To explore the influence of rapid urbanization development on the accumulation of 16 priority PAHs in urban environment, thirty-three surface sediments from city lakes in different urbanized areas of Shanghai were collected to evaluate the occurrence characteristic and source apportionment of PAHs. The concentrations of Σ₁₆PAHs in lake surface sediments ranged from 55.7 to 4928 ng g⁻¹ with a mean value of 1131 ng g⁻¹ (standard deviation, 1228 ng g⁻¹), of which 4-, 5- and 6-ring PAHs were the dominant components. Spatial distribution of PAHs in lake surface sediments showed a significantly declining trend along with a decreasing urbanization gradient (one-way ANOVA, p < .05). Two hotspots of sediment PAHs were mainly distributed at highly urbanized areas with intensive population density and heavy traffic activities and at burgeoning industrial towns in the suburb. Source apportionment of total PAHs identified by a constrained positive matrix factorization model revealed that vehicle emission and combustion of coal, biomass and natural gas were the absolutely predominant sources, respectively accounting for 55.0% and 40.45% of total PAHs burden in lake sediments. Land use regression (LUR) models were successfully developed to evaluate spatial variation of PAHs contamination in urban sediments based on their significant correlations with residential land, commercial land, traffic variables, industrial sources, and population density. All PAH compounds showed strong associations with one or two source indicators (the traffic congestion index and the number of industrial sources), with the fitting R² varying from 0.529 to 0.984. Our findings suggest that energy consumption related to land use activities obviously promoted PAH accumulations in urban sediment environment during rapid development of urbanization and industrialization in Shanghai.

Natural gas extraction (NGE) has expanded rapidly in the United States in recent years. Despite concerns, there is little information about the effects of NGE on air quality or personal exposures of people living or working nearby. Recent research suggests NGE emits polycyclic aromatic hydrocarbons (PAHs) into air. This study used low-density polyethylene passive samplers to measure concentrations of PAHs in air near active (n = 3) and proposed (n = 2) NGE sites. At each site, two concentric rings of air samplers were placed around the active or proposed well pad location. Silicone wristbands were used to assess personal PAH exposures of participants (n = 19) living or working near the sampling sites. All samples were analyzed for 62 PAHs using GC-MS/MS, and point sources were estimated using the fluoranthene/pyrene isomer ratio. ∑PAH was significantly higher in air at active NGE sites (Wilcoxon rank sum test, p < 0.01). PAHs in air were also more petrogenic (petroleum-derived) at active NGE sites. This suggests that PAH mixtures at active NGE sites may have been affected by direct emissions from petroleum sources at these sites. ∑PAH was also significantly higher in wristbands from participants who had active NGE wells on their properties than from participants who did not (Wilcoxon rank sum test, p < 0.005). There was a significant positive correlation between ∑PAH in participants' wristbands and ∑PAH in air measured closest to participants’ homes or workplaces (simple linear regression, p < 0.0001). These findings suggest that living or working near an active NGE well may increase personal PAH exposure. This work also supports the utility of the silicone wristband to assess personal PAH exposure.

Exposure to air pollutants such as volatile organic compounds (VOCs) and fine particulate matter (PM2.5) are associated with adverse health effects. This study applied multiple time resolution data of hourly VOCs and 24-h PM2.5 to a constrained Positive Matrix Factorization (PMF) model for source apportionment in Taipei, Taiwan. Ninety-two daily PM2.5 samples and 2208 hourly VOC measurements were collected during four seasons in 2014 and 2015. With some a priori information, we used different procedures to constrain retrieved factors toward realistic sources. A total of nine source factors were identified as: natural gas/liquefied petroleum gas (LPG) leakage, solvent use/industrial process, contaminated marine aerosol, secondary aerosol/long-range transport, oil combustion, traffic related, evaporative gasoline emission, gasoline exhaust, and soil dust. Results showed that solvent use/industrial process was the largest contributor (19%) to VOCs while the largest contributor to PM2.5 mass was secondary aerosol/long-range transport (57%). A robust regression analysis showed that secondary aerosol was mostly contributed by regional transport related factor (25%).