Quantifying Air Pollutant Emission from Agricultural Machinery Using Surveys—A Case Study in Anhui, China

Ai, Yi; Ge, Yunshan; Zheng, Ran; Li, Xueyao; Xu, Zheng; Chen, Yangfan; Miao, Xifeng; Xu, Xiaohong; Mao, Hongjun; Shi, Zongbo; Jin, Taosheng

doi:10.3390/atmos12040440

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…The diesel generators used to drive the grinding machines are no better. Ai et al (2012) reported that NO X and PM from agricultural machineries are equivalent to 17% and 22% of the total on-road tra c emissions in Anhui, China (Ai, et al, 2012). Aged vehicles emit signi cantly higher amounts and more toxic pollutants than newer ones due to changes in the properties of emissions such as hygroscopicity, particle size distribution, phase partitioning, morphology and chemical constitution (Liu, et al, 2019).…”

Section: Impact Assessment Of Cassava Processing Into Garri In the En...mentioning

confidence: 99%

Assessment Of Environmental Degradation Due To Processing Of Cassava Into Garri Flakes

Nnaji

Akanno

2022

Preprint

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The processing of cassava into garri is a multi-staged process that generates a large amount of effluent. This study was aimed at determining the impact of cassava processing into garri on the environment in Nsukka Metropolis. The soil, wastewater and sludge samples were collected from four different locations and subjected to physico-chemical analysis. The results obtained were subjected to descriptive statistical analysis and estimation of various measures of pollution indices. A detailed sanitary study of the processing plants was undertaken to ascertain their immediate and remote environmental impacts. The concentrations of hydrogen cyanide, total suspended solids and electrical conductivity were above the FEPA permissible limit and ranged from 8.6-42.8, 11900-40800 mg/l and 4006-5240 μS/cm respectively. Arsenic and mercury were above the FEPA permissible limit while the remaining heavy metals were within the permissible limits. Identified impacts from garri processing are: soil loss, litter generation, air pollution, water pollution, greenhouse gas emission, water demand and energy demand. The average pollution load index of the recipient soils ranged from moderately polluted (2.48) to highly polluted (4.56). Nemerow’s pollution index (NPI) indicated that soil samples from three of the locations were severely polluted (>3.0), while one was moderately polluted (2<PI<3). The geoaccumulation indices ranged from -0.03 (uncontaminated) to 2.59 (heavy contamination). The values of Er ranged from 5.63 to 91.8 for all metals investigated. The order of ecological risk of various metals based on their average values was: As>Cd>Cu>Cr>Pb>Ni with corresponding ecological risk values of 50.63 (moderate risk), 34.43 (low risk), 15.93 (low risk), 10.51 (low risk), 8.93 (low risk) and 5.63 (low risk).

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Section: Impact Assessment Of Cassava Processing Into Garri In the En...mentioning

confidence: 99%

Assessment Of Environmental Degradation Due To Processing Of Cassava Into Garri Flakes

Nnaji

Akanno

2022

Preprint

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“…As a result, pollutants can be transported by orographic flows to mountainous areas, arriving to significant concentrations in areas where they were not locally emitted. These constraints are reinforced by the fact, that, in Figure 8, higher NO2 concentrations coincide either with urbanized, industrialized zones (some of which are situated in areas with elevations of about 800 m or higher), or with areas of intensive agriculture (where fertilizers are sources of NO2, and diesel-powered machines are heavy NO2 producers); in some regions, both factors are summed up (Guo et al, 2020;Ai et al, 2021).…”

Section: Model Performance Assessmentmentioning

confidence: 99%

Satellite-based estimation of NO2 concentrations using a machine-learning model: a case study on Rio Grande do Sul, Brazil

Becerra-Rondón

Ducati

Haag

2022

ATM

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Nitrogen dioxide (NO2) is one of the most important atmospheric pollutants, affecting human health (increasing susceptibility to respiratory infections) and the environment (soil and water acidification). In many regions of Brazil, NO2 measurements at ground level meet difficulties because there are few and unevenly distributed monitoring stations. Satellite observations combined with machine learning models can mitigate this lack of data. This paper report results from an investigation on the ability of a machine learning approach (a non-linear statistical Random Forest algorithm, hereafter RF) to reconstruct the long-term spatiotemporal ground-level NO2 from 2006 to 2019 using as input parameters NO2 data retrieved from the Ozone Monitoring Instrument (OMI) sensor aboard AURA satellite, besides meteorological covariates and localized ground-level NO2 measurements. Results show that the RF model predicts NO2 with an accuracy expressed by an R2=0.68 correlation based on a 10-fold cross-validation. The model also predicted a mean NO2 concentration of 18.73 μg∕m3 (± 3.86 μg∕m3). The total NO2 concentration over the entire region analyzed showed a decreasing trend (2.9 μg/𝑚3 𝑦𝑟−1), being 2006 the year with the higher concentrations and 2017 with the lowest. This study suggests that non-linear statistical algorithm reconstructions using RF can be complementary tools to in situ and satellite observations to NO2 mapping.

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“…Questionnaires also provide data about fuel consumption during fieldwork, which is beneficial for the emission inventory accuracy [40,53]. The main problem with this approach is that conducting such a survey on an annual basis is difficult without government bodies due to funding demands [54]. The Netherlands [55] and Sweden [56] conduct such surveys, which enable them to develop Tier 3 emission inventories.…”

Section: Introductionmentioning

confidence: 99%

Development of a Spatial Tier 2 Emission Inventory for Agricultural Tractors by Combining Two Large-Scale Datasets

Lončarević,

Ilinčić,

Šagi

et al. 2023

Sustainability

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Agricultural tractor emissions have a notable influence on total emissions and climate change. This is especially important when developing an agricultural machinery emission inventory since tractors are the most widely used agricultural machinery. However, as with other agricultural machinery, there is a significant lack of data concerning tractors. This study combined two large-scale datasets to develop a Tier 2 tractor emission inventory for the Republic of Croatia. The first dataset, which was collected via a large-scale survey with 8895 respondents, includes data on fuel consumption by machine and engine type. The second dataset, which is official data of the Ministry of the Interior, includes data on machinery age. Since there is no direct link between datasets, data on fuel consumption were calculated for each of 21 Croatian counties and were then linked to the corresponding municipalities’ tractor age and power rating of the vehicle fleet. The result was used to calculate tractor emissions by with a Tier 2 approach using the method listed in the 2019 EMEP/EEA emissions guidebook. The calculations were made for five exhaust emission components: carbon dioxide (CO2), carbon monoxide (CO), hydrocarbon (HC), nitrogen oxide (NOX) and particulate matter (PM). The continental counties had the highest emissions for all emission types, especially the Osječko-baranjska and Vukovarsko-srijemska counties. The Tier 2 emission inventory was compared to data calculated using the Tier 1 method on a county level. There were notable differences between the inventories for HC and PM emissions, which is mainly due to Tier 2 approach having different emission factors depending on vehicle age. The end result was compared to the already existing Tier 1 emission inventory on a county level.

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Quantifying Air Pollutant Emission from Agricultural Machinery Using Surveys—A Case Study in Anhui, China

Cited by 11 publications

References 17 publications

Assessment Of Environmental Degradation Due To Processing Of Cassava Into Garri Flakes

Assessment Of Environmental Degradation Due To Processing Of Cassava Into Garri Flakes

Satellite-based estimation of NO2 concentrations using a machine-learning model: a case study on Rio Grande do Sul, Brazil

Development of a Spatial Tier 2 Emission Inventory for Agricultural Tractors by Combining Two Large-Scale Datasets

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