⛅ Four Cities, One Graph, One Question

Sailekshmi · May 30, 2026, 4:41pm

CUBE ChatShaala – Discussion Summary

Date: 30 May 2026

Today’s CUBE ChatShaala session brought together fourteen participants from diverse geographical locations across India, including Sailekshmi, Kiran, Arunan MC (Madel Thivim, Goa), Samiksha, Niharika, Dinesh, Susanta Tanti, Aarya, Himanshu Joshi, Akhil Gupta, Khushbu Gupta (Chaitanya Science & Arts College, Pamgarh), Manali Bhujade, Rechel Tirkey (Ranchi), and Saida. The central theme of the session revolved around a citizen science investigation into mosquito activity and its relationship with local temperature and time of day. The discussion was anchored in real observations, live weather data, and the construction of collaborative graphs on the shared whiteboard.

Mosquito Timing in Ulhasnagar (Maharashtra)

The session opened with attention drawn to a key observation: mosquito activity in Ulhasnagar has been recorded consistently between 8:30 PM and 9:00 PM. This narrow window was treated not as a coincidence but as a biological signal worth investigating. A graph was plotted with the time of day (Morning, Afternoon, Evening) on the X-axis and temperature (°C) on the Y-axis. Data points from Ulhasnagar showed temperatures hovering near 28°C during the afternoon, with the evening data point placed around 40°C (likely reflecting the peak heat accumulated during the day before a slight drop). Weather data for Ulhasnagar in late May indicates temperatures with highs around 35–36°C and lows around 29–30°C, consistent with the warm, humid conditions noted in the session. The observation that mosquitoes appear predominantly in the evening, rather than at peak afternoon temperatures, formed the intellectual seed of the day’s inquiry.

Multi-Location Temperature Data and Graph Construction

The most visually prominent feature of the whiteboard was a multi-location scatter plot comparing temperature patterns across four locations throughout the day (12 AM, 6 AM, 12 PM, 3 PM, 6 PM, 12 AM), each represented by a distinct colour-coded marker:

Pamgarh, Chhattisgarh (green X): Plotted as one of the hottest locations, consistent with current data. On 30 May, Pamgarh saw temperatures ranging between 26°C in the morning to peaks of 42°C–44°C in the evening, making it the hottest site among those compared.
Mangalapuram, Kerala (red X): Represented a moderately warm coastal pattern. The temperature in Mangalapuram today was around 33–34°C during the day, with a feels-like temperature of up to 38°C, and humidity at approximately 56%.
Bageshwar, Uttarakhand (blue X): Clearly the coolest location in the graph, with morning readings around 11–12°C on the scatter plot. Weather data for Bageshwar on 30 May confirms a maximum temperature of around 28°C (82°F) by early afternoon, with light fog in the early morning consistent with the lower early-morning plotted data points. Bageshwar’s participant also mentioned an expectation of rain from 4:00 PM to 9:00 PM, which tied nicely into a side discussion about whether rain affects mosquito emergence.
Ranchi, Jharkhand (orange X): Intermediate temperatures, but notably influenced by cloud cover and rainfall on this very day. In Ranchi, the maximum temperature on 30 May fell to 28°C while the minimum was 22°C, following a sharp drop of 4–5 degrees Celsius due to a weather system over northern and central India, with rain and thunderstorm alerts issued.

Participants actively contributed data from their own locations and plotted these points collaboratively. The resulting graph made visible what could easily be overlooked in isolation: temperature varies dramatically across India even on the same day, and this variation must be accounted for when thinking about mosquito behaviour at different sites.

Connecting Temperature to Mosquito Activity

The core scientific question threading through the session was: Is there a relationship between temperature and the time at which mosquitoes become active? The Ulhasnagar observer noted that mosquitoes consistently appear around 8:30–9:00 PM. When cross-referenced with the temperature graph, this timing corresponds to the tail end of the day’s warmth, not the hottest part, but perhaps the optimal zone. This prompted an implicit but powerful question: Does mosquito activity peak at a specific temperature threshold, or is it governed by the cooling rate after sunset? The group did not arrive at a firm answer, but the question was productively left open for further observation.

The session maintained the hallmark CUBE spirit: grounding abstract biological questions in personal, place-specific data, and using visual representations (graphs) to make patterns legible across locations.

Provocative Questions

Mosquito activity in Ulhasnagar is observed between 8:30 and 9:00 PM, which is after the peak afternoon temperature. Does this suggest that mosquitoes respond not to absolute temperature but to the rate of temperature change, i.e., the cooling gradient after sunset? How could participants design an experiment to test this?
The data from Pamgarh (Chhattisgarh) shows afternoon temperatures peaking near 42–44°C. If mosquitoes in Ulhasnagar are active at around 28–30°C in the evening, what temperature range would predict mosquito emergence in Pamgarh? Would a Pamgarh observer expect mosquitoes earlier or later in the evening?
Bageshwar, Uttarakhand, records early morning temperatures as low as 11–12°C. Would Aedes or Culex mosquitoes, the common species in CUBE studies, even be active at such temperatures? At what minimum temperature does mosquito flight activity cease?
Ranchi experienced a sharp temperature drop on 30 May due to approaching storms. Does sudden rain or a rapid temperature drop suppress mosquito activity, or does the increased humidity following rain actually encourage it?
The whiteboard graph shows temperature plotted at fixed time intervals (12 AM, 6 AM, 12 PM, 3 PM, 6 PM, 12 AM). Why might 3 PM and 6 PM data points be particularly significant for predicting mosquito emergence windows? What does the slope between these two points tell us biologically?
If we accept that mosquito timing is partly temperature-dependent, could the data collected across four Indian cities over a single day be used to predict mosquito activity windows for new, unstudied locations simply by knowing their temperature profile?
The Bageshwar participant expected rain from 4:00 PM to 9:00 PM. How does precipitation interact with standing water availability, larval development timelines, and adult mosquito activity? Would rain on a given evening delay or advance the appearance of mosquitoes that night?
CUBE’s citizen science model depends on participants making consistent, comparable observations from different places. What are the challenges of standardising “mosquito observation time” across locations in India where sunset times, local microhabitats, and building structures differ significantly?

What I Have Learned

Today’s session was a reminder of something genuinely exciting: that ordinary daily observations, noting when mosquitoes appear in your room or on your balcony, can become the raw material of serious scientific inquiry when systematically recorded and compared.

I learned that temperature alone does not tell the full story of mosquito activity. The timing of mosquito emergence in Ulhasnagar (8:30–9:00 PM) does not correspond to the hottest part of the day, which would be early-to-mid afternoon. Instead, it aligns with a transitional period of cooling, suggesting that the change in temperature, or the approach of a specific thermal threshold, may be more biologically meaningful than the absolute maximum.

I also learned the power of multi-location collaborative data in exposing variation. Looking at only one city’s temperature data could lead a researcher to assume that a particular mosquito behaviour is universal, when in fact, Pamgarh and Bageshwar offer almost opposite climatic conditions on the same day. Science conducted by a distributed community of observers, as CUBE does, is not just more inclusive; it is more accurate, because it captures genuine geographic and ecological diversity.

The session also reminded me that a well-constructed graph is a hypothesis made visible. When participants plotted their local temperature readings against time and compared them side by side, they were not just recording data; they were building a shared framework for asking the next question.

Finally, I was struck by the importance of local weather expectations. The Bageshwar participant’s knowledge that rain was anticipated that evening was itself a scientific variable, not just background information. In citizen science, the observer’s contextual knowledge is an asset, not noise.

TINKE Moments (This I Never Knew Earlier)

TINKE 1: “Temperature peaks in the afternoon, but mosquitoes appear in the evening — why?”
Several participants were implicitly assuming that mosquito activity would track peak temperature. The observation that Ulhasnagar mosquitoes consistently emerge hours after the temperature maximum challenged this assumption. This was a TINKE moment because it forced a more nuanced question: what environmental conditions at 8:30 PM make that window specifically favourable?

TINKE 2: “The same date, but wildly different temperatures across India”
When the multi-location scatter plot was drawn, it became visually clear that on 30 May 2026, Pamgarh was recording 40°C+ while Bageshwar was at 12°C in the early morning. For participants who may have thought of “today’s temperature” as a roughly uniform national condition, this was an eye-opening moment. Scientific observations cannot be decontextualised from geography.

TINKE 3: “Rain is not just weather — it is a variable”
The Bageshwar participant’s note about expected evening rain was initially framed as a weather update. But as the discussion progressed, it became clear that rain carries biological implications: it changes humidity, creates standing water, and may alter mosquito behaviour. Recognising rainfall as a scientific variable rather than a contextual aside was a productive TINKE.

TINKE 4: “A graph is not just a record — it is a question”
When the whiteboard graph was constructed with multiple location data points, some participants may have expected it to answer something. The TINKE moment came when it became clear that the graph’s primary function was to reveal what needed to be asked next — about patterns, outliers, and the meaning of differences.

Gaps and Misconceptions

Gap 1 – Humidity data is absent from the analysis. Temperature alone may be insufficient to model mosquito activity. Relative humidity, which was notably high in several locations today (Ulhasnagar at 75%, Ranchi at 51%, Mangalapuram at 56%), is a known co-determinant of mosquito flight activity. Future sessions would benefit from including humidity as a second Y-axis variable or a separate plot.

Gap 2 – Species identity is unspecified. The discussion of “mosquito timing” did not specify which mosquito species is being observed. Aedes aegypti, Culex quinquefasciatus, and Anopheles species have meaningfully different temperature and activity profiles. Without species identification, the observed timing patterns cannot be confidently attributed to biological mechanisms.

Gap 3 – “Evening” is not a precise enough unit. The whiteboard marks time categories broadly as Morning, Afternoon, and Evening. For the purposes of correlating mosquito emergence with temperature, finer time resolution (hourly readings) would be much more informative. The current resolution may mask the precise temperature at the moment of first mosquito appearance.

Misconception 1 – Higher temperature = more mosquitoes. There may be an implicit assumption among some participants that hotter locations will have more intense mosquito activity. This is not straightforwardly true; extreme heat (above 40°C) can actually suppress adult mosquito activity, and very high temperatures can also accelerate larval mortality. The relationship between temperature and mosquito abundance is non-linear, with an optimal range typically cited between 25°C and 30°C.

Misconception 2 – Rain always means more mosquitoes immediately. While rain creates larval habitats, the relationship between a rain event and immediate adult mosquito activity is not direct. Adult emergence from pupae takes days after water accumulation. Heavy rain may also temporarily suppress flying adult mosquitoes. The group should be careful not to conflate long-term ecological effects of rain with short-term changes in mosquito visibility on the same evening.

Misconception 3 – Temperature at one time of day represents the full picture. Using a single afternoon reading or a morning minimum as the representative “temperature” for a location glosses over the thermal range that organisms actually experience throughout the day. Mosquitoes respond to the full diurnal temperature cycle, and the evening cooling curve may be as important as the peak.