LIC AAO 2016: English Test – Day 19

The pilot phase of the much publicised odd-even scheme in Delhi is now over. Besides Delhi, comprehensive plans are urgently needed to improve air quality across several Indian cities, such as Patna, Gwalior, Raipur, Ahmedabad, Lucknow, Kanpur, Ludhiana and Amritsar. Although policy interventions primarily target air pollution, it is important to note that traffic congestion has significant economic costs to society. How did Delhi fare on metrics of air quality and congestion – and what lessons should other cities draw from the experiment?
The stationary air pollution monitors of the Delhi Pollution Control Committee (DPCC) indicate that average (ambient) PM 2.5 levels increased from 216 µg/m3 (26 – 31 Dec) to 331 µg/m3 (1 – 6 Jan) and subsequently declined to 308 µg/m3 (7 -11Jan). As per the Central Pollution Control Board (CPCB) PM 2.5 levels that exceed 250 µg/m3 correspond to an Air Quality Index of ‘Severe’. These levels affect healthy people and cause greater distress to those with existing heart or lung disease.
However, a spike in air pollution is not a predictor of failure just as a decline does not indicate policy success. This is because, in the short term, air pollution levels are dominated by meteorological conditions such as winds, rain and temperature.
While establishing a relationship between reduced car numbers and pollution levels is tenuous, the impact on traffic and congestion is purely a function of compliance levels. In order to understand the traffic mix during the odd-even experiment, the Council on Energy, Environment and Water (CEEW) monitored traffic volumes at five important stretches across New Delhi for three weeks (the week before and two weeks of the implementation).
In the morning peak (9 am – 11 am) we observed that overall vehicle counts increased (by 10%) in the two weeks of January, as compared to the last week of December. However, taxis, 2-wheelers, 3-wheelers and private buses contributed significantly to this increase. The increase ranges from 12% for 3-wheelers to 138% for private buses. This does suggest increased use (or availability) of these alternate modes of transport.
However, the number of private cars remained unchanged. Anecdotal experiences (documented in social media) suggest that commuters experienced lesser congestion on the roads. Congestion data (as recorded from Google Maps), on the other hand, suggests that the travel times were not statistically different between the last week of December and the first week of January. The available metrics are confounding and no definitive conclusion can be made, on the impact of the scheme on traffic and congestion as well. One conjecture is that the baseline chosen for comparison (last week of December) does not represent typical Delhi traffic.
Despite the short lead time available to prepare for a full-scale monitoring, we also installed low-cost pollution sensors at five locations. We find that the readings (for PM2.5) closely follow those from DPCC (located in the vicinity of our sensors). There is a need to create a network of such low-cost sensors across our cities, and to make residents aware of pollution levels in their respective localities.

Which of the following cities are needed comprehensive plans urgently to improve air quality?

The pilot phase of the much publicised odd-even scheme in Delhi is now over. Besides Delhi, comprehensive plans are urgently needed to improve air quality across several Indian cities, such as Patna, Gwalior, Raipur, Ahmedabad, Lucknow, Kanpur, Ludhiana and Amritsar. Although policy interventions primarily target air pollution, it is important to note that traffic congestion has significant economic costs to society. How did Delhi fare on metrics of air quality and congestion – and what lessons should other cities draw from the experiment?
The stationary air pollution monitors of the Delhi Pollution Control Committee (DPCC) indicate that average (ambient) PM 2.5 levels increased from 216 µg/m3 (26 – 31 Dec) to 331 µg/m3 (1 – 6 Jan) and subsequently declined to 308 µg/m3 (7 -11Jan). As per the Central Pollution Control Board (CPCB) PM 2.5 levels that exceed 250 µg/m3 correspond to an Air Quality Index of ‘Severe’. These levels affect healthy people and cause greater distress to those with existing heart or lung disease.
However, a spike in air pollution is not a predictor of failure just as a decline does not indicate policy success. This is because, in the short term, air pollution levels are dominated by meteorological conditions such as winds, rain and temperature.
While establishing a relationship between reduced car numbers and pollution levels is tenuous, the impact on traffic and congestion is purely a function of compliance levels. In order to understand the traffic mix during the odd-even experiment, the Council on Energy, Environment and Water (CEEW) monitored traffic volumes at five important stretches across New Delhi for three weeks (the week before and two weeks of the implementation).
In the morning peak (9 am – 11 am) we observed that overall vehicle counts increased (by 10%) in the two weeks of January, as compared to the last week of December. However, taxis, 2-wheelers, 3-wheelers and private buses contributed significantly to this increase. The increase ranges from 12% for 3-wheelers to 138% for private buses. This does suggest increased use (or availability) of these alternate modes of transport.
However, the number of private cars remained unchanged. Anecdotal experiences (documented in social media) suggest that commuters experienced lesser congestion on the roads. Congestion data (as recorded from Google Maps), on the other hand, suggests that the travel times were not statistically different between the last week of December and the first week of January. The available metrics are confounding and no definitive conclusion can be made, on the impact of the scheme on traffic and congestion as well. One conjecture is that the baseline chosen for comparison (last week of December) does not represent typical Delhi traffic.
Despite the short lead time available to prepare for a full-scale monitoring, we also installed low-cost pollution sensors at five locations. We find that the readings (for PM2.5) closely follow those from DPCC (located in the vicinity of our sensors). There is a need to create a network of such low-cost sensors across our cities, and to make residents aware of pollution levels in their respective localities.

Find the incorrect statement on the basis of the given passage?

The pilot phase of the much publicised odd-even scheme in Delhi is now over. Besides Delhi, comprehensive plans are urgently needed to improve air quality across several Indian cities, such as Patna, Gwalior, Raipur, Ahmedabad, Lucknow, Kanpur, Ludhiana and Amritsar. Although policy interventions primarily target air pollution, it is important to note that traffic congestion has significant economic costs to society. How did Delhi fare on metrics of air quality and congestion – and what lessons should other cities draw from the experiment?
The stationary air pollution monitors of the Delhi Pollution Control Committee (DPCC) indicate that average (ambient) PM 2.5 levels increased from 216 µg/m3 (26 – 31 Dec) to 331 µg/m3 (1 – 6 Jan) and subsequently declined to 308 µg/m3 (7 -11Jan). As per the Central Pollution Control Board (CPCB) PM 2.5 levels that exceed 250 µg/m3 correspond to an Air Quality Index of ‘Severe’. These levels affect healthy people and cause greater distress to those with existing heart or lung disease.
However, a spike in air pollution is not a predictor of failure just as a decline does not indicate policy success. This is because, in the short term, air pollution levels are dominated by meteorological conditions such as winds, rain and temperature.
While establishing a relationship between reduced car numbers and pollution levels is tenuous, the impact on traffic and congestion is purely a function of compliance levels. In order to understand the traffic mix during the odd-even experiment, the Council on Energy, Environment and Water (CEEW) monitored traffic volumes at five important stretches across New Delhi for three weeks (the week before and two weeks of the implementation).
In the morning peak (9 am – 11 am) we observed that overall vehicle counts increased (by 10%) in the two weeks of January, as compared to the last week of December. However, taxis, 2-wheelers, 3-wheelers and private buses contributed significantly to this increase. The increase ranges from 12% for 3-wheelers to 138% for private buses. This does suggest increased use (or availability) of these alternate modes of transport.
However, the number of private cars remained unchanged. Anecdotal experiences (documented in social media) suggest that commuters experienced lesser congestion on the roads. Congestion data (as recorded from Google Maps), on the other hand, suggests that the travel times were not statistically different between the last week of December and the first week of January. The available metrics are confounding and no definitive conclusion can be made, on the impact of the scheme on traffic and congestion as well. One conjecture is that the baseline chosen for comparison (last week of December) does not represent typical Delhi traffic.
Despite the short lead time available to prepare for a full-scale monitoring, we also installed low-cost pollution sensors at five locations. We find that the readings (for PM2.5) closely follow those from DPCC (located in the vicinity of our sensors). There is a need to create a network of such low-cost sensors across our cities, and to make residents aware of pollution levels in their respective localities.

Choose the word which is MOST SIMILAR in meaning to the word ‘ interventions ‘ as used in the passage?

The pilot phase of the much publicised odd-even scheme in Delhi is now over. Besides Delhi, comprehensive plans are urgently needed to improve air quality across several Indian cities, such as Patna, Gwalior, Raipur, Ahmedabad, Lucknow, Kanpur, Ludhiana and Amritsar. Although policy interventions primarily target air pollution, it is important to note that traffic congestion has significant economic costs to society. How did Delhi fare on metrics of air quality and congestion – and what lessons should other cities draw from the experiment?
The stationary air pollution monitors of the Delhi Pollution Control Committee (DPCC) indicate that average (ambient) PM 2.5 levels increased from 216 µg/m3 (26 – 31 Dec) to 331 µg/m3 (1 – 6 Jan) and subsequently declined to 308 µg/m3 (7 -11Jan). As per the Central Pollution Control Board (CPCB) PM 2.5 levels that exceed 250 µg/m3 correspond to an Air Quality Index of ‘Severe’. These levels affect healthy people and cause greater distress to those with existing heart or lung disease.
However, a spike in air pollution is not a predictor of failure just as a decline does not indicate policy success. This is because, in the short term, air pollution levels are dominated by meteorological conditions such as winds, rain and temperature.
While establishing a relationship between reduced car numbers and pollution levels is tenuous, the impact on traffic and congestion is purely a function of compliance levels. In order to understand the traffic mix during the odd-even experiment, the Council on Energy, Environment and Water (CEEW) monitored traffic volumes at five important stretches across New Delhi for three weeks (the week before and two weeks of the implementation).
In the morning peak (9 am – 11 am) we observed that overall vehicle counts increased (by 10%) in the two weeks of January, as compared to the last week of December. However, taxis, 2-wheelers, 3-wheelers and private buses contributed significantly to this increase. The increase ranges from 12% for 3-wheelers to 138% for private buses. This does suggest increased use (or availability) of these alternate modes of transport.
However, the number of private cars remained unchanged. Anecdotal experiences (documented in social media) suggest that commuters experienced lesser congestion on the roads. Congestion data (as recorded from Google Maps), on the other hand, suggests that the travel times were not statistically different between the last week of December and the first week of January. The available metrics are confounding and no definitive conclusion can be made, on the impact of the scheme on traffic and congestion as well. One conjecture is that the baseline chosen for comparison (last week of December) does not represent typical Delhi traffic.
Despite the short lead time available to prepare for a full-scale monitoring, we also installed low-cost pollution sensors at five locations. We find that the readings (for PM2.5) closely follow those from DPCC (located in the vicinity of our sensors). There is a need to create a network of such low-cost sensors across our cities, and to make residents aware of pollution levels in their respective localities.

Choose the word which is MOST OPPOSITE in meaning of the word ‘ congestion ‘ as used in the passage?

The pilot phase of the much publicised odd-even scheme in Delhi is now over. Besides Delhi, comprehensive plans are urgently needed to improve air quality across several Indian cities, such as Patna, Gwalior, Raipur, Ahmedabad, Lucknow, Kanpur, Ludhiana and Amritsar. Although policy interventions primarily target air pollution, it is important to note that traffic congestion has significant economic costs to society. How did Delhi fare on metrics of air quality and congestion – and what lessons should other cities draw from the experiment?
The stationary air pollution monitors of the Delhi Pollution Control Committee (DPCC) indicate that average (ambient) PM 2.5 levels increased from 216 µg/m3 (26 – 31 Dec) to 331 µg/m3 (1 – 6 Jan) and subsequently declined to 308 µg/m3 (7 -11Jan). As per the Central Pollution Control Board (CPCB) PM 2.5 levels that exceed 250 µg/m3 correspond to an Air Quality Index of ‘Severe’. These levels affect healthy people and cause greater distress to those with existing heart or lung disease.
However, a spike in air pollution is not a predictor of failure just as a decline does not indicate policy success. This is because, in the short term, air pollution levels are dominated by meteorological conditions such as winds, rain and temperature.
While establishing a relationship between reduced car numbers and pollution levels is tenuous, the impact on traffic and congestion is purely a function of compliance levels. In order to understand the traffic mix during the odd-even experiment, the Council on Energy, Environment and Water (CEEW) monitored traffic volumes at five important stretches across New Delhi for three weeks (the week before and two weeks of the implementation).
In the morning peak (9 am – 11 am) we observed that overall vehicle counts increased (by 10%) in the two weeks of January, as compared to the last week of December. However, taxis, 2-wheelers, 3-wheelers and private buses contributed significantly to this increase. The increase ranges from 12% for 3-wheelers to 138% for private buses. This does suggest increased use (or availability) of these alternate modes of transport.
However, the number of private cars remained unchanged. Anecdotal experiences (documented in social media) suggest that commuters experienced lesser congestion on the roads. Congestion data (as recorded from Google Maps), on the other hand, suggests that the travel times were not statistically different between the last week of December and the first week of January. The available metrics are confounding and no definitive conclusion can be made, on the impact of the scheme on traffic and congestion as well. One conjecture is that the baseline chosen for comparison (last week of December) does not represent typical Delhi traffic.
Despite the short lead time available to prepare for a full-scale monitoring, we also installed low-cost pollution sensors at five locations. We find that the readings (for PM2.5) closely follow those from DPCC (located in the vicinity of our sensors). There is a need to create a network of such low-cost sensors across our cities, and to make residents aware of pollution levels in their respective localities.

According to the author, what we need to installed in our cities for traffic control?

The pilot phase of the much publicised odd-even scheme in Delhi is now over. Besides Delhi, comprehensive plans are urgently needed to improve air quality across several Indian cities, such as Patna, Gwalior, Raipur, Ahmedabad, Lucknow, Kanpur, Ludhiana and Amritsar. Although policy interventions primarily target air pollution, it is important to note that traffic congestion has significant economic costs to society. How did Delhi fare on metrics of air quality and congestion – and what lessons should other cities draw from the experiment?
The stationary air pollution monitors of the Delhi Pollution Control Committee (DPCC) indicate that average (ambient) PM 2.5 levels increased from 216 µg/m3 (26 – 31 Dec) to 331 µg/m3 (1 – 6 Jan) and subsequently declined to 308 µg/m3 (7 -11Jan). As per the Central Pollution Control Board (CPCB) PM 2.5 levels that exceed 250 µg/m3 correspond to an Air Quality Index of ‘Severe’. These levels affect healthy people and cause greater distress to those with existing heart or lung disease.
However, a spike in air pollution is not a predictor of failure just as a decline does not indicate policy success. This is because, in the short term, air pollution levels are dominated by meteorological conditions such as winds, rain and temperature.
While establishing a relationship between reduced car numbers and pollution levels is tenuous, the impact on traffic and congestion is purely a function of compliance levels. In order to understand the traffic mix during the odd-even experiment, the Council on Energy, Environment and Water (CEEW) monitored traffic volumes at five important stretches across New Delhi for three weeks (the week before and two weeks of the implementation).
In the morning peak (9 am – 11 am) we observed that overall vehicle counts increased (by 10%) in the two weeks of January, as compared to the last week of December. However, taxis, 2-wheelers, 3-wheelers and private buses contributed significantly to this increase. The increase ranges from 12% for 3-wheelers to 138% for private buses. This does suggest increased use (or availability) of these alternate modes of transport.
However, the number of private cars remained unchanged. Anecdotal experiences (documented in social media) suggest that commuters experienced lesser congestion on the roads. Congestion data (as recorded from Google Maps), on the other hand, suggests that the travel times were not statistically different between the last week of December and the first week of January. The available metrics are confounding and no definitive conclusion can be made, on the impact of the scheme on traffic and congestion as well. One conjecture is that the baseline chosen for comparison (last week of December) does not represent typical Delhi traffic.
Despite the short lead time available to prepare for a full-scale monitoring, we also installed low-cost pollution sensors at five locations. We find that the readings (for PM2.5) closely follow those from DPCC (located in the vicinity of our sensors). There is a need to create a network of such low-cost sensors across our cities, and to make residents aware of pollution levels in their respective localities.

Choose the word which is MOST OPPOSITE in meaning of the word ‘ confounding ‘ as used in the passage?

The pilot phase of the much publicised odd-even scheme in Delhi is now over. Besides Delhi, comprehensive plans are urgently needed to improve air quality across several Indian cities, such as Patna, Gwalior, Raipur, Ahmedabad, Lucknow, Kanpur, Ludhiana and Amritsar. Although policy interventions primarily target air pollution, it is important to note that traffic congestion has significant economic costs to society. How did Delhi fare on metrics of air quality and congestion – and what lessons should other cities draw from the experiment?
The stationary air pollution monitors of the Delhi Pollution Control Committee (DPCC) indicate that average (ambient) PM 2.5 levels increased from 216 µg/m3 (26 – 31 Dec) to 331 µg/m3 (1 – 6 Jan) and subsequently declined to 308 µg/m3 (7 -11Jan). As per the Central Pollution Control Board (CPCB) PM 2.5 levels that exceed 250 µg/m3 correspond to an Air Quality Index of ‘Severe’. These levels affect healthy people and cause greater distress to those with existing heart or lung disease.
However, a spike in air pollution is not a predictor of failure just as a decline does not indicate policy success. This is because, in the short term, air pollution levels are dominated by meteorological conditions such as winds, rain and temperature.
While establishing a relationship between reduced car numbers and pollution levels is tenuous, the impact on traffic and congestion is purely a function of compliance levels. In order to understand the traffic mix during the odd-even experiment, the Council on Energy, Environment and Water (CEEW) monitored traffic volumes at five important stretches across New Delhi for three weeks (the week before and two weeks of the implementation).
In the morning peak (9 am – 11 am) we observed that overall vehicle counts increased (by 10%) in the two weeks of January, as compared to the last week of December. However, taxis, 2-wheelers, 3-wheelers and private buses contributed significantly to this increase. The increase ranges from 12% for 3-wheelers to 138% for private buses. This does suggest increased use (or availability) of these alternate modes of transport.
However, the number of private cars remained unchanged. Anecdotal experiences (documented in social media) suggest that commuters experienced lesser congestion on the roads. Congestion data (as recorded from Google Maps), on the other hand, suggests that the travel times were not statistically different between the last week of December and the first week of January. The available metrics are confounding and no definitive conclusion can be made, on the impact of the scheme on traffic and congestion as well. One conjecture is that the baseline chosen for comparison (last week of December) does not represent typical Delhi traffic.
Despite the short lead time available to prepare for a full-scale monitoring, we also installed low-cost pollution sensors at five locations. We find that the readings (for PM2.5) closely follow those from DPCC (located in the vicinity of our sensors). There is a need to create a network of such low-cost sensors across our cities, and to make residents aware of pollution levels in their respective localities.

Which of the following is ‘true’ in the context of the passage?

The pilot phase of the much publicised odd-even scheme in Delhi is now over. Besides Delhi, comprehensive plans are urgently needed to improve air quality across several Indian cities, such as Patna, Gwalior, Raipur, Ahmedabad, Lucknow, Kanpur, Ludhiana and Amritsar. Although policy interventions primarily target air pollution, it is important to note that traffic congestion has significant economic costs to society. How did Delhi fare on metrics of air quality and congestion – and what lessons should other cities draw from the experiment?
The stationary air pollution monitors of the Delhi Pollution Control Committee (DPCC) indicate that average (ambient) PM 2.5 levels increased from 216 µg/m3 (26 – 31 Dec) to 331 µg/m3 (1 – 6 Jan) and subsequently declined to 308 µg/m3 (7 -11Jan). As per the Central Pollution Control Board (CPCB) PM 2.5 levels that exceed 250 µg/m3 correspond to an Air Quality Index of ‘Severe’. These levels affect healthy people and cause greater distress to those with existing heart or lung disease.
However, a spike in air pollution is not a predictor of failure just as a decline does not indicate policy success. This is because, in the short term, air pollution levels are dominated by meteorological conditions such as winds, rain and temperature.
While establishing a relationship between reduced car numbers and pollution levels is tenuous, the impact on traffic and congestion is purely a function of compliance levels. In order to understand the traffic mix during the odd-even experiment, the Council on Energy, Environment and Water (CEEW) monitored traffic volumes at five important stretches across New Delhi for three weeks (the week before and two weeks of the implementation).
In the morning peak (9 am – 11 am) we observed that overall vehicle counts increased (by 10%) in the two weeks of January, as compared to the last week of December. However, taxis, 2-wheelers, 3-wheelers and private buses contributed significantly to this increase. The increase ranges from 12% for 3-wheelers to 138% for private buses. This does suggest increased use (or availability) of these alternate modes of transport.
However, the number of private cars remained unchanged. Anecdotal experiences (documented in social media) suggest that commuters experienced lesser congestion on the roads. Congestion data (as recorded from Google Maps), on the other hand, suggests that the travel times were not statistically different between the last week of December and the first week of January. The available metrics are confounding and no definitive conclusion can be made, on the impact of the scheme on traffic and congestion as well. One conjecture is that the baseline chosen for comparison (last week of December) does not represent typical Delhi traffic.
Despite the short lead time available to prepare for a full-scale monitoring, we also installed low-cost pollution sensors at five locations. We find that the readings (for PM2.5) closely follow those from DPCC (located in the vicinity of our sensors). There is a need to create a network of such low-cost sensors across our cities, and to make residents aware of pollution levels in their respective localities.

Choose the word which is MOST SIMILAR in meaning to the word ‘ compliance’ as used in the passage?

The pilot phase of the much publicised odd-even scheme in Delhi is now over. Besides Delhi, comprehensive plans are urgently needed to improve air quality across several Indian cities, such as Patna, Gwalior, Raipur, Ahmedabad, Lucknow, Kanpur, Ludhiana and Amritsar. Although policy interventions primarily target air pollution, it is important to note that traffic congestion has significant economic costs to society. How did Delhi fare on metrics of air quality and congestion – and what lessons should other cities draw from the experiment?
The stationary air pollution monitors of the Delhi Pollution Control Committee (DPCC) indicate that average (ambient) PM 2.5 levels increased from 216 µg/m3 (26 – 31 Dec) to 331 µg/m3 (1 – 6 Jan) and subsequently declined to 308 µg/m3 (7 -11Jan). As per the Central Pollution Control Board (CPCB) PM 2.5 levels that exceed 250 µg/m3 correspond to an Air Quality Index of ‘Severe’. These levels affect healthy people and cause greater distress to those with existing heart or lung disease.
However, a spike in air pollution is not a predictor of failure just as a decline does not indicate policy success. This is because, in the short term, air pollution levels are dominated by meteorological conditions such as winds, rain and temperature.
While establishing a relationship between reduced car numbers and pollution levels is tenuous, the impact on traffic and congestion is purely a function of compliance levels. In order to understand the traffic mix during the odd-even experiment, the Council on Energy, Environment and Water (CEEW) monitored traffic volumes at five important stretches across New Delhi for three weeks (the week before and two weeks of the implementation).
In the morning peak (9 am – 11 am) we observed that overall vehicle counts increased (by 10%) in the two weeks of January, as compared to the last week of December. However, taxis, 2-wheelers, 3-wheelers and private buses contributed significantly to this increase. The increase ranges from 12% for 3-wheelers to 138% for private buses. This does suggest increased use (or availability) of these alternate modes of transport.
However, the number of private cars remained unchanged. Anecdotal experiences (documented in social media) suggest that commuters experienced lesser congestion on the roads. Congestion data (as recorded from Google Maps), on the other hand, suggests that the travel times were not statistically different between the last week of December and the first week of January. The available metrics are confounding and no definitive conclusion can be made, on the impact of the scheme on traffic and congestion as well. One conjecture is that the baseline chosen for comparison (last week of December) does not represent typical Delhi traffic.
Despite the short lead time available to prepare for a full-scale monitoring, we also installed low-cost pollution sensors at five locations. We find that the readings (for PM2.5) closely follow those from DPCC (located in the vicinity of our sensors). There is a need to create a network of such low-cost sensors across our cities, and to make residents aware of pollution levels in their respective localities.

What is the central theme of the passage?

The pilot phase of the much publicised odd-even scheme in Delhi is now over. Besides Delhi, comprehensive plans are urgently needed to improve air quality across several Indian cities, such as Patna, Gwalior, Raipur, Ahmedabad, Lucknow, Kanpur, Ludhiana and Amritsar. Although policy interventions primarily target air pollution, it is important to note that traffic congestion has significant economic costs to society. How did Delhi fare on metrics of air quality and congestion – and what lessons should other cities draw from the experiment?
The stationary air pollution monitors of the Delhi Pollution Control Committee (DPCC) indicate that average (ambient) PM 2.5 levels increased from 216 µg/m3 (26 – 31 Dec) to 331 µg/m3 (1 – 6 Jan) and subsequently declined to 308 µg/m3 (7 -11Jan). As per the Central Pollution Control Board (CPCB) PM 2.5 levels that exceed 250 µg/m3 correspond to an Air Quality Index of ‘Severe’. These levels affect healthy people and cause greater distress to those with existing heart or lung disease.
However, a spike in air pollution is not a predictor of failure just as a decline does not indicate policy success. This is because, in the short term, air pollution levels are dominated by meteorological conditions such as winds, rain and temperature.
While establishing a relationship between reduced car numbers and pollution levels is tenuous, the impact on traffic and congestion is purely a function of compliance levels. In order to understand the traffic mix during the odd-even experiment, the Council on Energy, Environment and Water (CEEW) monitored traffic volumes at five important stretches across New Delhi for three weeks (the week before and two weeks of the implementation).
In the morning peak (9 am – 11 am) we observed that overall vehicle counts increased (by 10%) in the two weeks of January, as compared to the last week of December. However, taxis, 2-wheelers, 3-wheelers and private buses contributed significantly to this increase. The increase ranges from 12% for 3-wheelers to 138% for private buses. This does suggest increased use (or availability) of these alternate modes of transport.
However, the number of private cars remained unchanged. Anecdotal experiences (documented in social media) suggest that commuters experienced lesser congestion on the roads. Congestion data (as recorded from Google Maps), on the other hand, suggests that the travel times were not statistically different between the last week of December and the first week of January. The available metrics are confounding and no definitive conclusion can be made, on the impact of the scheme on traffic and congestion as well. One conjecture is that the baseline chosen for comparison (last week of December) does not represent typical Delhi traffic.
Despite the short lead time available to prepare for a full-scale monitoring, we also installed low-cost pollution sensors at five locations. We find that the readings (for PM2.5) closely follow those from DPCC (located in the vicinity of our sensors). There is a need to create a network of such low-cost sensors across our cities, and to make residents aware of pollution levels in their respective localities.

Choose the word which is MOST SIMILAR in meaning to the word ‘ conjecture’ as used in the passage?