Vehicle Stock Numbers and Survival Functions for On-road Exhaust Emissions Analysis in India: 1993-2018

Guttikunda, S. K. Vehicle Stock Numbers and Survival Functions for On-road Exhaust Emissions Analysis in India: 1993-2018. Final publication online @ https://www.mdpi.com/2071-1050/16/15/6298 Preprints 2024, 2024051393. https://doi.org/10.20944/preprints202405.1393.v1 Older publication:Re-fueling road transport for better air quality in Indiahttps://www.sciencedirect.com/science/article/abs/pii/S0301421514000020 An informed emissions inventory can help define the baseline, use that baseline to formulate an effective air quality management plan and track progress or lack thereof, and use the results for research, innovation, and public awareness. In air quality management, at urban and regional levels, road transport remains the cornerstone of residential, commercial, and industrial activities, and the vehicle exhaust emissions maintain the position of one of the key contributing sources. In Indian cities, big and small, vehicle exhaust emissions and dust from vehicle movement on the roads, contribute to as much as 50% of particulate matter pollution in a year. So, having access to a vehicle exhaust emissions inventory that is reliable and replicable is critical for air quality management and one of the key inputs to this exercise is vehicle stock numbers. These numbers are typically obtained from vehicle registration databases, traffic surveys, and other governmental records, and often require time consuming data cleaning protocols before they can used for emissions analysis. This database provides a clean and open-access vehicle stock database, as registered and in-use fleet, for the period covering 1993 and 2018, for all India and states, along with an estimate of age-mix of the vehicles using survival functions. VAPIS Excel players included here are ·A method to convert fleet average speeds and fleet average travel time per day into vehicle km travelled per day. ·A method to calculate how many additional buses are required to support odd-even or an equivalent scheme (with and without fuel mix exemptions). ·A method to calculate total fuel wasted from idling in the city and to calculate savings from traffic management. ·A method to calculate fuel and emission benefits of shifting a share of 2-wheeler and 4-wheeler trips to buses and non-motorized transport. ·A method to estimate vehicle exhaust emission factors using emission standards and deterioration rates. ·An example set of survival rates based on vehicle age for nine broad vehicle categories to convert registered number of vehicles to in-use number of vehicles. ·A method to spatially disaggregate (grid) the total vehicle exhaust emissions using multiple grid-level proxies as weights such as density (km per grid) of various road types, population density, landuse-landcover, and information on commercial and industrial activities. ·A library of emission factors for aerosols and gaseous species.古蒂昆达 S.K. 所著《印度道路尾气排放分析用车辆保有量与存活函数（survival functions）：1993-2018》，该论文最终正式版本在线发布于：https://www.mdpi.com/2071-1050/16/15/6298；2024年发布预印本，编号为2024051393，DOI：10.20944/preprints202405.1393.v1。 早期出版物：《优化道路交通以改善印度空气质量》，在线链接：https://www.sciencedirect.com/science/article/abs/pii/S0301421514000020。 一份详实的机动车尾气排放清单（emissions inventory）可用于确立基准基线，依托该基线制定高效的空气质量管控方案并追踪实施进展与不足，其成果还可用于科研创新与公众科普。在城市及区域层面的空气质量管控中，道路交通始终是居民生活、商业运营与工业活动的核心支撑，而机动车尾气排放则是关键污染源之一。 印度各大小城市中，机动车尾气排放与道路扬尘每年可贡献高达50%的颗粒物污染。因此，获取可靠且可复现的机动车尾气排放清单对空气质量管控至关重要，而该工作的核心输入之一便是车辆保有量数据（vehicle stock numbers）。此类数据通常源自机动车注册数据库、交通调查及其他政府记录，且往往需要经过耗时的数据清洗流程后方可用于排放分析。 本数据集提供了1993年至2018年间印度全国及各邦的注册与在用机动车保有量数据库，该库经过清洗且可开放获取，同时基于存活函数（survival functions）估算了机动车的车龄结构（age-mix）。 本数据集内置以下VAPIS Excel工具集： · 一种将车队平均车速与日均平均行驶时长转换为日均车辆行驶里程的方法。 · 一种用于计算支撑单双号限行或等效限行政策（含/不含燃料类型豁免）所需新增公交车辆数的方法。 · 一种用于测算城市机动车怠速工况下的总燃油损耗，并量化交通管理措施可带来的燃油节约量的方法。 · 一种用于测算将部分两轮、四轮机动车出行转移至公共交通或非机动交通（non-motorized transport）后，可获得的燃油与排放削减效益的方法。 · 一种基于排放标准与车辆老化速率估算机动车尾气排放因子（emission factors）的方法。 · 覆盖9大类机动车的基于车龄的存活速率示例集，用于将注册车辆数转换为在用车辆数。 · 一种以多类网格级代理变量为权重，将机动车总尾气排放进行空间网格化分配的方法，所用权重包括各类道路的网格密度（公里/网格）、人口密度、土地利用/覆被以及工商业活动信息。 · 气溶胶与气态污染物排放因子数据库。