I’ve recently updated the Sydney temperature, sea level and rainfall pages to include the latest data available from the BOM. In case you’ve not seen these before a quick explanation:
The BOM provides long term weather observations for the Observatory Hill weather station going back as far as 1859. It’s an amazing resource, when graphed over time you start to see some cool trends emerge. I’ve set it up so you can graph the raw observations or select to see moving averages from 6 months up to 30 years. For the shorter time periods the observations can be all over the place, even the 10 year averages fluctuate up and down but selecting the 30 year averages show clear trends. Except for rainfall! In Sydney at least it’s all over the place with no clear trend over the last 160 years, there are 10yr or so patterns that i think correspond to El Nino and La Nina events.
After last years terrible fire season I got interested in particulate matter air pollution from smoke and other sources and It’s effect on health. Inspired by this post I ended up buying some parts so that I could put together my own PM2.5 and PM10 detector. There are plenty of ready made detectors you can buy but I figured if I made my own I’d be able to customise it and add functionality that you’d normally have to pay a premium for. It’s also a lot of fun to build these projects and learn along the way!
The core of the detector is an old Raspberry Pi2. For the actual pollution measurement I used a SDS011 particulate matter detector. I also had a tiny little OLED display left over from another project that was used for a simple screen. I wrote some Python code to read the pollution values from the detector, display them on the screen and also post them to an online data recording platform. Initially I used code from the the raspberrypi.org post to read values from the detector. It worked but it was simplistic. It had the detector and the fan running around the clock even though I was only taking a reading every 5 minutes. After learning that the SDS011 has a limited operational life of 8000 hours I decided to take a different approach. By issuing commands to the detector it’s possible to only switch it on when making a reading and then switch it off again. The SDS011 is a popular part and there were already several Python libraries out there that handled the low level details, perfect! I went with Ivan Kalchev’s py-sds011 Running the detector, screen and Python code required some setup and configuration of the Raspberry Pi, I automated this using Ansible so the details would be saved and could then quickly be rerun again as needed. Here’s what I did
OK, back to the fire. The burn was over a 33ha area of nearby bushland with the closest point only 2 streets away. As it started on the Friday morning huge plumes of smoke appeared, for most of the day air quality at our house actually was fine. All the smoke went straight up into the sky. At around 5pm that changed.
I had the detector running inside the house with all windows closed. PM2.5 levels quickly rose to about 300, the smell was awful and it became very hazy, ash was dropping from the sky. It was still like this when I went to bed.
At 4am Saturday morning I was woken with a sore throat and a heavy feeling in my chest, I got up and the whole house was full of smoke, the indoor PM2.5 reading was almost 1000 at that point! I got up put on a mask and went for a walk around the streets and through to bush to take some photos. The air must have cooled overnight and brought the smoke down with it. It was pretty interesting that it was not evenly distributed, some streets were thick with it others relatively clear.
The smoke started to clear at around 8am, levels dropped rapidly over the next hour then by 10am it was on the way to normal.
As bad as it was it was over reasonably quickly and it was important to get the burn done to reduce the chances of an out of control fire. The PM numbers were sobering though, even short term exposure to elevated PM2.5 results in increased mortality from cardiac arrest, especially for people over 65 and other vulnerable members of the community. Smoke from the 2019/2020 summer fires killed almost 450 people, more than 10x the fires themselves.
The NSW government provides hourly, daily and monthly pollution data. The nearest monitoring station is at Macquarie Park is 6.5km away, on the day of the smoke it measured virtually no elevation above normal background levels for PM2.5 or PM10.
I’m going to try to keep my detector running full time and make the readings public, here are the live daily and weekly stats.
There’s no shortage of sandstone in the Sydney area, almost the entire extent of Garigal National Park sits on whats known as the Hawkesbury Sandstone. The Sydney 1:100 000 Geological Sheet classifies it as “Medium to course-grained quartz sandstone, very minor shale and laminite lenses” I wanted to highlight two sandstone formations, both of which are a bit of a mystery as to how they form.
I came across these formations on a trail run on a section of single track in Belrose that runs from the end of Ralston Ave down to the Bare Creek trail next to Bare Creek.
The first formation is called tessellated pavement.
There are a number of different types of tessellated pavement. The type I came across is seen on flat sections of sandstone that have been fractured into 4-6 sided geometric shapes. Some of the blocks are surrounded by deep grooves with rounded edges. It really looks like a man made road or path the way the blocks lock together. It’s not known how this structure forms.
The second formation is even more of a mystery.
It was on the same section of trail near the tessellated pavement. Found on a gently sloping section of sandstone, it had rows of deep grooves all running in the same direction from the top of the slope to the bottom. They looked like they could have been worn over many years by the trickle of water. Above the trail was a minor gully, there wasn’t any visible creek but it looked damp and swampy. In wetter times perhaps water could have drained slowly over the sandstone. This is all guessing, I couldn’t find any information on this type of formation. The closest I came was rillenkarren or rundkarren which is the weathering of similar channels in limestone by the slight acidity of the water dissolving the rock. Rillenkarren has sharper ridges and is thought to form out in the open while rundkarren is more rounded and thought to form under a superficial covering like sandy till, peat or a layer of plants and lichen.
I’ve updated the climate pages with the latest data from the BOM.
If this summer felt long and hot to you then you’re right. The graph shows the average summer high is the hottest it’s been for 5 years. Looking at the 1 year averages the cooling trend that started in 2014 has reversed and we’re warming again. As always the longer term 30 year averages still show a very clear and consistent warming trend. The warming acceleration that started in 2013 is continuing.