Spatio-temporal variation of in-cabin PM 2.5 levels in Metro Rolling Stock Saloons, Kochi Metro, South India

doi:10.21203/rs.3.rs-2119804/v1

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Spatio-temporal variation of in-cabin PM 2.5 levels in Metro Rolling Stock Saloons, Kochi Metro, South India

https://doi.org/10.21203/rs.3.rs-2119804/v1

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Increasing particulate matter (PM) especially PM_2.5 (particles less than 2.5µm in aerodynamic diameter), pollution in the air-conditioned indoor microenvironment can spread infectious diseases such as Covid-19 on occupants in addition to respiratory and cardiovascular diseases. Given the importance of this subject in the contemporary social context, this study seeks to assess the spatiotemporal variation of PM_2.5 concentration in Metro Rolling Stock (MRS) saloons of Kochi Metro Rail Limited (KMRL), Kerala, India. During the in-transit survey data was collected according to the four different plans depending on the time and direction of the metro service using a real-time Indoor Air Quality (IAQ) monitor. The spatial variation in PM_2.5 content en-route 22 stations (Aluva to Petta) shows, Kalamassery Town station recorded the maximum value of 40.33µg/m³ and station Maharajas Ground recorded the lowest value of 23.95µg/m³ respectively. Comparing the median concentration of PM_2.5 of the four plans implied that plan-1 (Aluva-Petta during morning peak hours) record the highest concentration than other plans (p-value < 0.05). Principal Component Analysis biplots, did not display strong positive associations among PM_2.5 and the other in-transit variables such as temperature, relative humidity, carbon dioxide concentration, and the number of occupants. Therefore, the study infers that the infiltration of outdoor air when the doors are opened at each station could be the influencing factor of PM_2.5 level in MRS saloons of KMRL. Moreover, the proximity to traffic signals, industrial establishments, and muncipal waste dumping sites increase the probability of PM_2.5 level which was evident at Kalamassery Town station.

In-cabin air quality

Particulate matter

Metro Rolling Stock saloons

In-transit survey

Principal Component Analysis

Metro rail vehicles are commonly called cabins, but they are technically known as Metro Rolling Stock (MRS) saloons. They have been considered as the most appropriate transportation system for urban commuters. The MRS saloons can provide a time-bound and comfortable journey and reduce superficial traffic congestion and gasoline consumption (Xu, and Hao, 2017). According to the World Metro figure, 2018 approximately 168 metro systems operate in 56 different countries. In addition, about 53.76 billion passengers use these systems annually. In general, the ‘Heating Ventilation and Air-Conditioning’ (HVAC) systems which belong to the category of ‘SID-3300R’, and relevant sensors have been equipped in all MRS saloons to provide a comfortable journey (thermal) and improve the quality of indoor environment. This HVAC system has three main functional compartments: supply fan compartment, mixed air compartment, and condenser compartment. Through the employment of these three functional compartments, the system can help control and optimize the indoor comfort level (temperature, relative humidity, concentration of carbon dioxide) and receive sufficient fresh air from the ambient environment by considering the number of passengers and the climate. In addition, during the travelling time, all doors and windows of MRS saloons are closed to avoid contact with outside air and to allow air circulation to be fully controlled by the HVAC system. Despite all this attention, particle contamination in MRS saloons remains a significant concern around the world.

PM_2.5 is one of the significant pollutants which reduces the air quality inside MRS saloons. According to the United States Environmental Protection Agency (EPA) standards, the acceptable limit for PM_2.5 concentration in the indoor environment is 35µg/m³ within 24 hours. As more people are utilizing the public transportation system, the quality of the microenvironment in MRS saloons with regards to PM_2.5 concentration is a concern especially in the contemporary social context of Covid-19. The human respiratory system can not filter PM_2.5, and due to lower size of particles, it can easily enter the lungs and mix with blood. This situation can lead to numerous respiratory-cardiovascular diseases in commuters and staff (Tan et al., 2017; Kumar et al., 2018; Betancourt et al., 2019; Onat et al., 2019; Camacho et al., 2020; Manojkumar et al., 2021; Maji et al., 2021). In addition, increasing the concentration of PM_2.5 in the indoor environment can accelerate the spread of airborne infectious diseases like Nasopharyngitis, Influenza, Chickenpox, Measles, and Covid-19 etc (DeFelice et al, 2020; Wang et al., 2020 (A); Wang et al., 2020 (B); Zoran et al., 2020). Some recent studies about the spread of coronavirus (SARS-CoV-2) in the indoor environment reveal that the increase of PM_2.5 concentration has been positively correlated with the extent of Covid-19; moreover, they suggested that the increase in just 1µg/m³ of PM_2.5 corresponding to a 15% increase in Covid-19 deaths (Mehmood et al., 2020; Xie et al., 2020; Wu et al., 2020). The piston wind is a phenomenon commonly experienced in underground or tunnel-shaped metro stations; which is a primary source of PM_2.5 in the MRS saloons. The piston wind is formed due to the friction between the metro rolling stock's braking systems, wheels, and rails (Martins et al., 2016; Minguillon et al., 2018). Therefore, the piston wind contains various elements like Fe, Cr, Ca, Al, Na, Ba, Mn, Zn, Cu, Ni, Co, Pb etc (Mohsen et al., 2018; Chen et al., 2020; Roy et al., 2020). In addition, many other factors such as passenger density, operational time, ambient concentration of PM_2.5, and seasonal variations, directly or indirectly influence the PM_2.5 level in MRS saloons (Chang et al., 2021). The added advantage of using the portable IAQ monitor during the in-cabin air quality studies can accurately understand the PM_2.5 concentration and its spatio-temporal variation which can speed up the mitigation/ policy-making process (Xu et al., 2017; Kumar et al, 2018; Zhang et al., 2020). The present study aims to investigate the spatio-temporal variation of PM_2.5 concentration in metro rolling stock saloons of Kochi Metro Rail Limited, Kerala, India using IAQ monitor (ENVITUS ESAIA1001W, AirU, India). The other variables such as indoor temperature, relative humidity, carbon dioxide concentration and the number of occupants were investigated for statistical correlation studies through an in-transit survey using a locally made real-time IAQ monitor. Ambient PM_2.5 concentration, temperature and humidity have been measured from three air quality monitoring stations (Vytila, Kacheripady, and Eloor) to determine the impact of these parameters on the air quality of the in-cabin environment of MRS saloons. Most importantly, this is one of the pioneer studies of in-cabin air quality concerning the spatio-teporal variation of PM_2.5 levels in MRS saloons, India.

2.1. Study area

Kochi is a commercial city in Kerala, India, notable for its trade, industry and tourism, has flourished under the foreign domination of the Portuguese, Dutch and English. Today, the city has an advanced public transportion system with a single line metro working under the Kochi Metro Rail Limited (KMRL). The Kochi Metro has made a huge impact on the public as it represents a time-bound and convenient journey while avoiding traffic congestion in the city. The KMRL is one of the latest metro systems in India, which is located in the centre of Ernakulam district, Kerala state. It is a 26.78 km long corridor that connects the north and south parts of the district. Since 17th June 2017, the KMRL has been started its service from Aluva to Palarivattom with 11 stations. Currently, the service has extended from Aluva to Petta with 22 stations (Fig. 1). The exact geographical location of the stations is furnished in Table 1. Kochi metro rail interconnects six municipalities and 33 villages in the district. Each rolling stocks of KMRL has three cabins with 136 seats and can hold a maximum of 975 passengers on board. As per the annual report of KMRL, 2020, before covid-19 nation-wide lockdown in India, it was estimated that over 2 Million passengers (20,74,430) used its services. Considering the ongoing pandemic situation due to Covid-19, KMRL has taken all the safety measures including thermal screening, contactless ticketing, regular sanitisation of trains and stations, and ensured social distancing. In this juncture, understanding and reducing the particle concentration especialy PM_2.5 inside the MRS saloons will help improve indoor air quality and avoid the condition of bioburden (Wu et al., 2020).

Table 1

Geographical position of KMRL stations
Stations	Latitude (N)	Longitude (E)
Aluva	10° 6'35.32"N	76°20'58.21"E
Pulinchodu	10° 5'42.29"N	76°20'47.82"E
Companypady	10° 5'14.28"N	76°20'34.03"E
Ambattukavu	10° 4'45.77"N	76°20'19.94"E
Muttom	10° 4'22.15"N	76°20'1.51"E
Kalamassery Town	10° 3'31.64"N	76°19'19.21"E
CUSAT	10° 2'48.56"N	76°19'5.76"E
Pathadipalam	10° 2'10.54"N	76°18'51.82"E
Edappally OPPO	10° 1'31.13"N	76°18'30.34"E
Changambuzha Park	10° 0'55.02"N	76°18'8.30"E
Palarivattom	10° 0'23.39"N	76°18'17.17"E
JLN Stadium	10° 0'1.29"N	76°17'56.12"E
Kaloor	9°59'40.16"N	76°17'29.36"E
Town hall	9°59'29.23"N	76°17'18.17"E
M G Road	9°59'0.43"N	76°16'56.44"E
Maharajas Ground	9°58'23.42"N	76°17'6.70"E
Ernakulam South	9°58'6.78"N	76°17'21.41"E
Kadavanthra	9°57'59.75"N	76°17'53.44"E
Elamkulam	9°58'1.74"N	76°18'29.80"E
Vytila	9°58'2.87"N	76°19'13.92"E
Thykoodam	9°57'36.05"N	76°19'25.04"E
Petta	9°57'9.17"N	76°19'48.55"E

2.2. In-transit survey using IAQ monitoring system

The study was carried out to assess the spatio-temporal fluctuations in the concentrations of PM_2.5 and influence of other variables on PM_2.5 ditribution. The use and acceptance of indoor air quality (IAQ) monitoring systems will enhance the ability to comprehend live information in a timely manner and to make decisions/policies quickly (Li et al., 2017). Therefore, the present study used a real-time IAQ system (ENVITUS ESAIA1001W, AirU, India) which works on the principle of light scattering measurement of PM_2.5 concentration. The light scattering theory and microcontroller is intended to solve the problems of complicated operation, difficult portability, lagging monitoring results, and medium consumption of the existing PM_2.5 detector (Wang et al., 2019). The in-transit survey was conducted on six non-consecutive days between 9th March to 16th March, 2021 on the regular service of the KMRL during the morning (7–11 am) and evening peak hours (4–8 pm) respectively. The KMRL has 25 rolling stocks which operate along 22 stations. Each rolling stock runs from Aluva to Petta and vice versa. The rolling stock has a halt of ten to fifteen seconds for pick up and drop off passengers at each station. Extra time is spent at stations only during peak hours. Studies have shown that dust particles enter the cabins from the platforms when the saloon doors are open (Onat and Stakeeva., 2013; Zhang et al., 2020). Based on the previous study the data collection was done by placing the IAQ monitor at a height of 1.5 m near the door according to Li et al., 2018. The data collection for the in-transit survey was carried out based on the following plans:

Plan-1: Morning peak hours starting from Aluva and ends at Petta (MPH A-P).

Plan-2: Morning peak hours starting from Petta and ends at Aluva (MPH P-A).

Plan 3: Evening peak hours starting from Aluva and ends at Petta (EPH A-P).

Plan 4: Evening peak hours starting from Petta and ends at Aluva (EPH P-A).

The in-transit data collection was standardized for all 24 trips using the same methodology. The higher and lower PM_2.5 concentrations among stations are determined based on each plan during the in-transit data collection. The premises of each station were directly assessed along with the in-transit survey to assess the potential and sources of PM_2.5 concentration. In addition, necessary data were collected to determine the source of PM_2.5 and evaluation using appropriate statistical methods.

2.3. Statistical analysis

Pearson correlation analysis, and Principal component analysis was used for deriving statistical inferences. Through these comparative analysis, it has been possible to understand exactly what is the criterion or source for the increase in the concentration of PM_2.5 in Kochi metro rolling stock saloons. At the same time, by distinguishing between the four plans arranged according to spatio-temporal variation, it is necessary to understand the stations that are prone to particle (PM_2.5) pollution with reference to time. By differentiating between the four plans it is possible to know which plan is most affected by PM_2.5 pollution. Further, due to the non-normal distribution of data (Shapiro-Wilk test; p < 0.05), the Kruskal-Wallis rank-sum test was implemented to compare the significant difference between plans in their median PM_2.5 concentration. It is followed by the Pairwise Wilcoxon Rank Sum Test with Bonferroni correction to find out the pair of plans between which there is a significant difference.

3.1. Spatial variation in PM_2.5 levels

The data obtained during the in-transit survey were computed and represented graphically (Fig. 2). Based on the six day avarage concentration of PM_2.5 obtained en-route 22 stattions of KMRL, the highest level of 40.33µg/m³ was obtained at Kalamassery Town and lowest of 23.95µg/m³ was observed at Maharajas College Ground. Aluva with 31.33µg/m³ and Pulinchodu with 31.25µg/m³ stations are ranked second and third. The acceptable threshold limit of PM_2.5 concentration (35µg/m³; EPA, 2014) exceeded only at Kalamassery Town station.

3.2. Statistical analysis

3.2.1. Correlation with number of commuters

During the in-transit survey, traffic restrictions were in place as part of Covid-19 prevention activities. Therefore, the number of passengers has been decreased significantly compared to the previous years (KMRL, 2020). The study hypothesized that the decrease in commuters number would be directly proportional to the concentration of PM_2.5. To verify this, the data obtained from the in-transit survey has been subjected to statistical analyses. As per the initial hypothesis which says "The decrease in the number of passengers may be the reason for the decrease in the concentration of PM_2.5 in metro saloons" has been validated through Pearson correlation coefficient (r). The ‘r’ value of each plan are: 0.40, -0.58, -0.26, -0.38 respectively (Table.2). These values indicate a moderate positive (negative) linear relationship through fuzzy-firm linear rule (Ratner, B. 2009). Therefore, the number of occupants does not influence the PM_2.5 concentration, but the variation observed in PM_2.5 concentration observed in KMRL may be due to some other variables. This can be accurately defined by comparing and statistically analysisng all the parameters obtained during the in-transit survey, including the PM_2.5 concentration.

Table 2

Descriptive statistics derived for each plans
	Variables	N	Mean	SD	Sum	Min	Max	(r) value	p-value
Plan-1	PM_2.5	22	35.81	5.59	788	27	44	0.40	0.063
Plan-1	No.of Occupants	22	17.75	6.65	390.50	5	26	0.40	0.063
Plan-2	PM_2.5	22	29.77	2.33	655	25	33	-0.58	0.004
Plan-2	No.of Occupants	22	17.43	7.49	383.50	4.8	29	-0.58	0.004
Plan-3	PM_2.5	22	22.31	4.11	491	17.33	33.66	-0.26	0.242
Plan-3	No.of Occupants	22	22.26	7.82	489.83	7.66	33.66	-0.26	0.242
Plan-4	PM_2.5	22	20.38	2.27	448.50	17	26.33	-0.38	0.081
Plan-4	No.of Occupants	22	26.50	12.18	583.17	4.83	42.83	-0.38	0.081

Note: Variables:- N- number of cells, Mean- mean value of each variable, SD- standard deviation of each variable, Sum- total value of each variable, Min- lowest value of variable, Max- highet value of variable, (r) value- pearson correlation coefficient value, (p-value)- the probability that you would have found the current result if the correlation coefficient were in fact zero.

3.2.2. Principal component analysis (PCA)

In the present study, according to the fuzzy-firm linear rule, the fluctuations in the number of occupants did not significantly influence the concentration of PM_2.5. To understand the underlying pattern in the multivariate dataset Principal component analysis (PCA) was conducted. Kwon et al. (2015) estimated a correlation matrix incorporating a total of 12 variables including PM_2.5 and experimental parameters, and calculated the eigen values of all PCs.. In the PCA biplot (Fig. 3) each dot in the biplot represents one sample (i.e., station). Stations which are similar in terms of the values of corresponding variable have been placed closer to one another and those which are dissimilar farther apart. In the PCA biplot, Principal Component 1 (PC-1) and Principal Component 2 (PC-2) together explains 84.44% (62.82% + 21.62%) of variance in the data set. Furthe, stations (or samples) which are located in the increasing direction of loading vectors tend to have higher values for that particular variable (Jackson., 1991; Johnson and Whicherm, 2007). While considering the underlying correlations between variables, Relative humidity and CO₂ were found to be positively correlated with No. of occupants, whereas PM_2.5 showed no correlation with No. of occupants. Also, the biplot shows that the PCA-Biplot; No. of occupants; and relative humidity are higher at stations Edappally OPPO, and Changampuzha, as they are located along the increasing direction of the loading vector —No. of occupants. Similarly, Changampuzha and Palarivattom stations recorded higher values for CO₂ content with respect to number occupants. Among the 22 stations, the PM_2.5 concentration obtained at Kalamassery station was the highest, but at the same time the PCA correlation study between the PM_2.5 concentration and no. of occupants revealed a negative correlation. As there was no other sources of indoor PM_2.5 in MRS saloons, the results clearly indicated the influence of ambient air quality in the immediate sorrounding of the Kalamassery stations. The earlier studies conducted by (Kim et al., 2011), and (Kim et al., 2012) validates the above findings.

Table 3

Descriptive statistics of whole in-transit data
	PM_2.5	CO₂	Temperature	Relative humidity	No.of occupants
N analysis	22	22	22	22	22
N missing	0	0	0	0	0
Mean	27.67	1048.72	27.58	61.23	20.99
Standard Deviation	3.75	81.33	0.41	1.43	8.05

Table 4

Correlation matrix
	PM_2.5	CO₂	Temperature	Relative humidity	No.of occupants
PM_2.5	1	-0.05	-0.18	0.06	-0.01
CO2	-0.05	1	-0.42	0.87	0.96
Temperature	-0.18	-0.42	1	-0.50	-0.49
Relative humidity	0.06	0.87	-0.50	1	0.91
No.of occupants	-0.01	0.96	-0.49	0.91	1

Table 5

Eigenvalues of correlation matrix
	1	2	3	4	5
Eigenvalue	3.14123	1.08078	0.61866	0.13067	0.02867
Percentage of Variance	62.82%	21.62%	12.37%	2.61%	0.57%
Cumulative	62.82%	84.44%	96.81%	99.43%	100.00%

Table 6

Extracted eigenvectors
	PM_2.5	CO₂	Temperature	Relative humidity	No.of occupants
Coefficients of PC1	0.03135	0.53309	-0.35789	0.53413	0.54904
Coefficients of PC2	0.90913	-0.16961	-0.36514	-0.02125	-0.10457

3.2.3. Variation in PM_2.5 concentration between Indoor and Outdoor environments.

The ambient (Outdoor) information about PM_2.5 concentration, temperature, and relative humidity of Kochi city were collected from three different ambient air quality monitoring stations near the metro line such as Eloor, Kacheripady, and Vytila respectively. The data for ambient air quality were collected from World air quality index project (aqicn.org-waqi.info), and weather data service (visualcrossing.com) respectively and which were represented in Table.3. The PM_2.5 abundance was found to be normally distributed in both indoor and outdoor environments (Shapiro-Wilk test, p > 0.05). Therefore Unpaired two-sample t-test (t = -6.3648, df = 10) was performed to assess whether the PM_2.5 concentrations differ significantly between these two environments. The test results showed a significant difference (p-value = 8.195e-05) between indoor and outdoor environments in their mean PM_2.5 concentration.

Table 7

The indoor/outdoor values of three variables
	Date	Temperature (⁰C)	Relative humidity(%)	PM_2.5 (µg/m³)
Outdoor	09-03-2021	28.77	73.90	85.67
	10-03-2021	28.33	71.88	63.67
	11-03-2021	27.47	73.95	65.67
	13-03-2021	29.20	68.52	120.67
	14-03-2021	29.17	72.01	96.67
	16-03-2021	29.07	72.48	109.67
Indoor	09-03-2021	28.67	60.24	30.94
	10-03-2021	27.13	61.41	20.91
	11-03-2021	27.05	61.76	14.94
	13-03-2021	27.80	59.51	29.84
	14-03-2021	28.17	60.83	30.41
	16-03-2021	26.68	63.63	35.40

3.2.4. Variation in PM_2.5 concentration between different plans.

Since the data were found to be non-normal following the Shapiro-Wilk test, the Kruskal-Wallis rank-sum test (non-parametric counterpart of one-way ANOVA) was implemented to compare the difference in PM_2.5 concentration between groups.

(MPAP [Morning Peak Hour (Aluva-Petta)] = 36.83 µg/m3; MPPA [Morning Peak Hour (Petta-Aluva)] = 29.83 µg/m³ ; EPAP [Evening Peak Hour (Aluva-Petta)] = 21.83 µg/m³; EPPA [Evening Peak Hour (Petta-Aluva)] = 20.25 µg/m³.

Kruskal-Wallis test (chi-squared = 62.429, df = 3) showed a significant difference between groups (p-value = 1.779e-13) in their median PM_2.5 concentration. Therefore, Pairwise Wilcoxon Rank Sum Test with Bonferroni correction was applied to find out the pair of groups(s) between which there is a significant difference.

Table 8

Pairwise Wilcoxon Rank Sum Test results
	EPAP	EPPA	MPAP
EPPA	0.17685	-	-
MPAP	2.4e-07	5.6e-08	-
MPPA	2.6e-06	5.6e-08	0.00073

The Boxplot representation shows that the concentration of PM_2.5 is highest in plan-1 which infered that there is a significant difference in concentration from other plans. Therefore, a higher concentration of PM_2.5 has been observed in the journey from Aluva to Petta during the morning peak hours.

The present study monitored the PM_2.5 concentration, and its Spatio-temporal variation was evaluated in MRS saloons of KMRL between 9th and 16th March 2021. Additionally, the sources of PM_2.5 and its indoor and outdoor relationship analysis were also carried out. Correlation between PM_2.5 concentration and the number of occupants indicated that the increase in the number of passengers did not significantly affect the PM_2.5 concentration. This may be due to covid-19 restrictions put forward in KMRL operations, and the number of commuters was substantially less during the in-transit survey.

The multivariate statistical analysis revealed that the concentration of PM_2.5 has no strong association with other variables. This may be due to the influence of outdoor sources of PM_2.5. In addition, the PCA biplots revealed that the Kalamassery town station recorded a higher concentration of PM_2.5. Which is a clear indication of the outdoor sources of PM_2.5 concentration as this metro station is near industrial establishments, crematoriums, and municipal waste dumping site.

The findings were justified in the correlation study between indoor and outdoor air quality parameters of PM_2.5 concentration. The outdoor PM_2.5 concentration was the dominant factor that influenced the indoor PM_2.5 concentration at all stations. Among the four plans in-transit survey study, the highest concentration of PM_2.5 was observed during the morning peak hours of service from Aluva to Petta. However, comparatively lower concentration of PM_2.5 was recorded during evening peak hours.

Except for Kalamassery station, no other station was exceeded the threshold limit for PM_2.5 concentration, and many stations have recorded very low PM_2.5 levels. Therefore the possibility of covid-19 spread due to increased concentration of PM_2.5 does not exist in MRS saloons of KMRL. The study was conducted during the covid-19 period which was significant for the control of indoor air quality by The KMRL authorities and to substantiate the exact relationship between PM_2.5 concentration and other variables, the detailed investigation has to be conducted after lifting the covid-19 restrictions.

Aknowledgements

The study was supported by the Cochin University of Science and Technology and funded by (e-grantz 3.0) the programme of the Government of Kerala, India. The authors would like to acknowledge the officials of Kochi Metro Rail Limited for their cooperation and assistance during the field measurments. Special thanks to project assistants who participated in the in-transit survey.

Data availability

All data available in manuscript.

Funding

This work has been funded by e-grantz 3.0 programme, Government of Kerala, India.

Author Contributions

Kottikkathara Bhanuvikraman Akhilesh: Conceptualization, Investigation, Methodology, Data Analysis, Writing- original draft

Thirumalammal Akhila: Investigation, Methodology, Writing - review editing

Palat Kannankai Madhuraj: Data Analysis, Writing - review editing

Thaitharanikathil Babu Anjali: Writing- review & editing

Madhavan Anand: Conceptualization, Funding acquisition, Investigation, Supervision Writing- review & editing

All authors read and approved the final manuscript

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

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No competing interests reported.

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Spatio-temporal variation of in-cabin PM 2.5 levels in Metro Rolling Stock Saloons, Kochi Metro, South India

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methodology

2.1. Study area

2.2. In-transit survey using IAQ monitoring system

2.3. Statistical analysis

3. Results And Discusion

3.1. Spatial variation in PM_2.5 levels

3.2. Statistical analysis

3.2.1. Correlation with number of commuters

3.2.2. Principal component analysis (PCA)

3.2.3. Variation in PM_2.5 concentration between Indoor and Outdoor environments.

3.2.4. Variation in PM_2.5 concentration between different plans.

4. Conclusion

Declarations

References

Additional Declarations

Status:

Version 1

Spatio-temporal variation of in-cabin PM 2.5 levels in Metro Rolling Stock Saloons, Kochi Metro, South India

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methodology

2.1. Study area

2.2. In-transit survey using IAQ monitoring system

2.3. Statistical analysis

3. Results And Discusion

3.1. Spatial variation in PM2.5 levels

3.2. Statistical analysis

3.2.1. Correlation with number of commuters

3.2.2. Principal component analysis (PCA)

3.2.3. Variation in PM2.5 concentration between Indoor and Outdoor environments.

3.2.4. Variation in PM2.5 concentration between different plans.

4. Conclusion

Declarations

References

Additional Declarations

Status:

Version 1

3.1. Spatial variation in PM_2.5 levels

3.2.3. Variation in PM_2.5 concentration between Indoor and Outdoor environments.

3.2.4. Variation in PM_2.5 concentration between different plans.