Methane emissions from natural gas vehicles in China

Natural gas vehicles (NGVs) have been promoted in China to mitigate air pollution, yet our measurements and analyses show that NGV growth in China may have significant negative impacts on climate change. We conducted real-world vehicle emission measurements in China and found high methane emissions from heavy-duty NGVs (90% higher than current emission limits). These emissions have been ignored in previous emission estimates, leading to biased results. Applying our observations to life-cycle analyses, we found that switching to NGVs from conventional vehicles in China has led to a net increase in greenhouse gas (GHG) emissions since 2000. With scenario analyses, we also show that the next decade will be critical for China to reverse the trend with the upcoming China VI standard for heavy-duty vehicles. Implementing and enforcing the China VI standard is challenging, and the method demonstrated here can provide critical information regarding the fleet-level CH4 emissions from NGVs.


Statistics
For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.

n/a Confirmed
The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.

For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings
For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection

Data analysis
For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.

Data
Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Mark A. Zondlo Da Pan Jun 30, 2020 10 Hz observations of methane and carbon dioxide concentrations were collected using commercial software from Ll-COR Biosciences, (Li7700_win-1.0.18.exe and li7500rs_win-6.5.2.exe). 10 Hz observations of ammonia, carbon monoxide, and GPS location were collected using a custom LabVIEW code (LV12) .
Anaconda Python 2.7 distribution (Anaconda2-2019.10-Windows-x86_64) was used to analyze the data. The codes were provided with the supplementary data and were archived in https://github.com/dp7-PU/CH4_from_NGV_in_China. Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

Reporting for specific materials, systems and methods
We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.
During the 2014 CAREBEIJING North China Plain field campaign, we deployed a mobile laboratory to quantify methane emissions from natural gas vehicles in China. Our mobile laboratory was equipped with fast response laser-based sensors to measure methane emissions from exhaust and leakage from natural gas buses and taxis in Baoding and Shijiazhuang.
We captured emissions from 73 natural gas buses and 63 natural gas taxis during the field campaign. Out of the 73 natural gas buses, 39 of them were powered by liquefied natural gas and 34 were powered by compressed natural gas. The samples were randomly selected in the field to represent fleet level emissions in the two cities (~1000 for NG buses and~2000 for NG taxis). The buses and taxis in the cities represent typical engine and emission control technologies in the country at the time of the field campaign (China Emission Standard V).
Vehicles were followed randomly to measure real-world emissions. The sample size was determined by counting the number of NGVs along this road and balanced by measurement constraints such as distance, drive time, battery capacities for the sensing package, and meteorological considerations. The sample size was so far the largest to our knowledge, not only for similar studies for NGVs in China but also for NGVs in other regions. Our results show small variations across the daily results and small differences between vehicles powered by compressed natural gas and liquefied natural gas, indicating the representativeness of our samples. We also surveyed engine types used by natural gas vehicles in China, which shows the sampled vehicles in the two cities represent the typical technology used in China (China V standard), justifying our extrapolation of our results to national level.
Da Pan, Kang Sun, and Lei Tao drove the mobile lab on-road to measure emissions from vehicles.
We measured 26 hours on road, covering around 600 km in these two cities on June 10th, 11th, and 12th in 2014. Each measurements includes 10s to 200s measurement time, corresponds several hundred meters to several kilometers in spatial range. This spatial scale represents typical urban driving cycle.
No data were excluded from data analysis.
We sampled on different days and different locations to improve the representativeness of our observations. The time between measuring an individual NGV ranges from few tens of seconds to an hour depending on the traffic conditions. As mentioned before, the observed variation was very small.
The vehicles were followed randomly on-road.
Since the vehicles were followed randomly on-road, we considered our measurements blinded to the vehicle owners.
Ambient temperature was around 30 deg with no precipitation.
We did not import or export samples.
No disturbance was caused by our study.